Model: "unet" ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ │ input_layer │ (None, 128, 128, │ 0 │ - │ │ (InputLayer) │ 3) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d (Conv2D) │ (None, 128, 128, │ 1,792 │ input_layer[0][0] │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalization │ (None, 128, 128, │ 256 │ conv2d[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_1 (Conv2D) │ (None, 128, 128, │ 36,928 │ leaky_re_lu[0][0] │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 128, 128, │ 256 │ conv2d_1[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_1 │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d │ (None, 64, 64, │ 0 │ leaky_re_lu_1[0]… │ │ (MaxPooling2D) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_2 (Conv2D) │ (None, 64, 64, │ 73,856 │ max_pooling2d[0]… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_2[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_2 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_3 (Conv2D) │ (None, 64, 64, │ 147,584 │ leaky_re_lu_2[0]… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_3[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_3 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_1 │ (None, 32, 32, │ 0 │ leaky_re_lu_3[0]… │ │ (MaxPooling2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_4 (Conv2D) │ (None, 32, 32, │ 295,168 │ max_pooling2d_1[… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_4[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_4 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_5 (Conv2D) │ (None, 32, 32, │ 590,080 │ leaky_re_lu_4[0]… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_5[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_5 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_2 │ (None, 16, 16, │ 0 │ leaky_re_lu_5[0]… │ │ (MaxPooling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_6 (Conv2D) │ (None, 16, 16, │ 1,180,160 │ max_pooling2d_2[… │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_6[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_6 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_7 (Conv2D) │ (None, 16, 16, │ 2,359,808 │ leaky_re_lu_6[0]… │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_7[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_7 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ max_pooling2d_3 │ (None, 8, 8, 512) │ 0 │ leaky_re_lu_7[0]… │ │ (MaxPooling2D) │ │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_8 (Conv2D) │ (None, 8, 8, │ 4,719,616 │ max_pooling2d_3[… │ │ │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 8, 8, │ 4,096 │ conv2d_8[0][0] │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_8 │ (None, 8, 8, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_9 (Conv2D) │ (None, 8, 8, │ 9,438,208 │ leaky_re_lu_8[0]… │ │ │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 8, 8, │ 4,096 │ conv2d_9[0][0] │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_9 │ (None, 8, 8, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d │ (None, 16, 16, │ 0 │ leaky_re_lu_9[0]… │ │ (UpSampling2D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate │ (None, 16, 16, │ 0 │ up_sampling2d[0]… │ │ (Concatenate) │ 1536) │ │ leaky_re_lu_7[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_10 (Conv2D) │ (None, 16, 16, │ 7,078,400 │ concatenate[0][0] │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_10[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_10 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_11 (Conv2D) │ (None, 16, 16, │ 2,359,808 │ leaky_re_lu_10[0… │ │ │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 16, 16, │ 2,048 │ conv2d_11[0][0] │ │ (BatchNormalizatio… │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_11 │ (None, 16, 16, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d_1 │ (None, 32, 32, │ 0 │ leaky_re_lu_11[0… │ │ (UpSampling2D) │ 512) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate_1 │ (None, 32, 32, │ 0 │ up_sampling2d_1[… │ │ (Concatenate) │ 768) │ │ leaky_re_lu_5[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_12 (Conv2D) │ (None, 32, 32, │ 1,769,728 │ concatenate_1[0]… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_12[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_12 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_13 (Conv2D) │ (None, 32, 32, │ 590,080 │ leaky_re_lu_12[0… │ │ │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 32, 32, │ 1,024 │ conv2d_13[0][0] │ │ (BatchNormalizatio… │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_13 │ (None, 32, 32, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d_2 │ (None, 64, 64, │ 0 │ leaky_re_lu_13[0… │ │ (UpSampling2D) │ 256) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate_2 │ (None, 64, 64, │ 0 │ up_sampling2d_2[… │ │ (Concatenate) │ 384) │ │ leaky_re_lu_3[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_14 (Conv2D) │ (None, 64, 64, │ 442,496 │ concatenate_2[0]… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_14[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_14 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_15 (Conv2D) │ (None, 64, 64, │ 147,584 │ leaky_re_lu_14[0… │ │ │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 64, 64, │ 512 │ conv2d_15[0][0] │ │ (BatchNormalizatio… │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_15 │ (None, 64, 64, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ up_sampling2d_3 │ (None, 128, 128, │ 0 │ leaky_re_lu_15[0… │ │ (UpSampling2D) │ 128) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ concatenate_3 │ (None, 128, 128, │ 0 │ up_sampling2d_3[… │ │ (Concatenate) │ 192) │ │ leaky_re_lu_1[0]… │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_16 (Conv2D) │ (None, 128, 128, │ 110,656 │ concatenate_3[0]… │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 128, 128, │ 256 │ conv2d_16[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_16 │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_17 (Conv2D) │ (None, 128, 128, │ 36,928 │ leaky_re_lu_16[0… │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ batch_normalizatio… │ (None, 128, 128, │ 256 │ conv2d_17[0][0] │ │ (BatchNormalizatio… │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ leaky_re_lu_17 │ (None, 128, 128, │ 0 │ batch_normalizat… │ │ (LeakyReLU) │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ dropout (Dropout) │ (None, 128, 128, │ 0 │ leaky_re_lu_17[0… │ │ │ 64) │ │ │ ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ │ conv2d_18 (Conv2D) │ (None, 128, 128, │ 130 │ dropout[0][0] │ │ │ 2) │ │ │ └─────────────────────┴───────────────────┴────────────┴───────────────────┘ Total params: 31,402,562 (119.79 MB) Trainable params: 31,390,786 (119.75 MB) Non-trainable params: 11,776 (46.00 KB) Epoch 1/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 9:50 5s/step - accuracy: 0.4373 - loss: 1.0242 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3223  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.4494 - loss: 1.0001 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3195  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.4612 - loss: 0.9758 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3174  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.4729 - loss: 0.9511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3150  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.4899 - loss: 0.9247 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3128  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.5081 - loss: 0.8983 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3110  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.5264 - loss: 0.8731 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3097  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.5444 - loss: 0.8490 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3091  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.5617 - loss: 0.8261 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3089  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.5772 - loss: 0.8056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3088  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.5916 - loss: 0.7865 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3090  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.6051 - loss: 0.7685 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3094  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.6176 - loss: 0.7516 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3099  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.6293 - loss: 0.7358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3106  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.6402 - loss: 0.7208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3114  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.6504 - loss: 0.7067 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3123  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.6600 - loss: 0.6933 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3132  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.6689 - loss: 0.6806 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3142  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.6773 - loss: 0.6687 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3153  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.6852 - loss: 0.6572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3163  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.6926 - loss: 0.6464 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3175  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.6996 - loss: 0.6360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3186  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.7062 - loss: 0.6262 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3197  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.7125 - loss: 0.6168 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3209  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.7184 - loss: 0.6079 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3220  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.7240 - loss: 0.5993 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3231  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.7293 - loss: 0.5911 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3243  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.7344 - loss: 0.5832 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3255  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.7392 - loss: 0.5757 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3266  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.7439 - loss: 0.5684 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3277  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.7483 - loss: 0.5614 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3289  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.7525 - loss: 0.5546 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3300  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.7565 - loss: 0.5481 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3311  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.7604 - loss: 0.5418 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3322  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.7641 - loss: 0.5357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3333  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.7677 - loss: 0.5299 - mean_absolute_error: 0.5000 - 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━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.7918 - loss: 0.4893 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3423  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.7943 - loss: 0.4849 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3433  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.7968 - loss: 0.4806 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3442  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.7992 - loss: 0.4764 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3451  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.8015 - loss: 0.4723 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3460  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.8038 - loss: 0.4684 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3468  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.8059 - loss: 0.4645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3477  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - 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mean_squared_error: 0.3590  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.8337 - loss: 0.4139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3597  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.8351 - loss: 0.4113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3603  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.8365 - loss: 0.4087 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3610  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.8378 - loss: 0.4062 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3616  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.8391 - loss: 0.4038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3622  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.8404 - loss: 0.4013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3628  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.8417 - loss: 0.3990 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3634  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.8429 - loss: 0.3966 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3640  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.8441 - loss: 0.3943 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3646  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.8452 - loss: 0.3921 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3652  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.8464 - loss: 0.3899 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3658  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.8475 - loss: 0.3877 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3663  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.8486 - loss: 0.3856 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3669  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.8497 - loss: 0.3835 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3675  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - 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━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.8698 - loss: 0.3434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3784 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.8705 - loss: 0.3420 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3789 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.8712 - loss: 0.3405 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3793 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.8719 - loss: 0.3391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3797 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.8726 - loss: 0.3377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3801 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.8732 - loss: 0.3363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3805 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.8739 - loss: 0.3350 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3809 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.8746 - loss: 0.3336 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3813 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.8752 - loss: 0.3323 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3816 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.8758 - loss: 0.3310 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3820 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.8764 - loss: 0.3297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3824 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.8771 - loss: 0.3284 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3828 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.8777 - loss: 0.3271 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3831 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.8783 - loss: 0.3259 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3835 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.8789 - loss: 0.3246 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3839 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.8794 - loss: 0.3234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3842 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.8800 - loss: 0.3222 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3846 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.8806 - loss: 0.3210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3849 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.8811 - loss: 0.3198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3853 120/120 ━━━━━━━━━━━━━━━━━━━━ 41s 301ms/step - accuracy: 0.9475 - loss: 0.1797 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4268 - val_accuracy: 0.9608 - val_loss: 0.1818 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4336 Epoch 2/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9707 - loss: 0.1068 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4601  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9703 - loss: 0.1071 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4615  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9705 - loss: 0.1059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4621  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9713 - loss: 0.1036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4627  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9718 - loss: 0.1019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4631  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9722 - loss: 0.1005 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4634  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9725 - loss: 0.0993 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4636  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9729 - loss: 0.0983 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4638  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9732 - loss: 0.0973 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4640  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9734 - loss: 0.0966 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4641  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9735 - loss: 0.0962 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4642  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9736 - loss: 0.0958 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4643  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9737 - loss: 0.0955 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4643  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9737 - loss: 0.0953 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4643  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9738 - loss: 0.0951 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4644  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9738 - loss: 0.0949 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4644  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9739 - loss: 0.0947 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4645  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9739 - loss: 0.0945 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4645  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9740 - loss: 0.0943 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4646  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9740 - loss: 0.0940 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4647  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9741 - loss: 0.0938 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4647  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9742 - loss: 0.0936 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4648  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9742 - loss: 0.0934 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4648  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9742 - loss: 0.0933 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4648  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9743 - loss: 0.0932 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4649  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9743 - loss: 0.0930 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4649  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9743 - loss: 0.0930 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4649  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9743 - loss: 0.0928 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4650  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9743 - loss: 0.0927 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4650  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9744 - loss: 0.0926 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4650  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9744 - loss: 0.0925 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4651  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9744 - loss: 0.0923 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4651  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9745 - loss: 0.0922 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4652  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9745 - loss: 0.0921 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4652  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9745 - loss: 0.0920 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4652  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9745 - loss: 0.0919 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4653  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9745 - loss: 0.0918 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4653  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9746 - loss: 0.0917 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4653  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9746 - loss: 0.0916 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4654  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9746 - loss: 0.0915 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4654  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9746 - loss: 0.0914 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4654  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9746 - loss: 0.0913 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4655  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9746 - loss: 0.0912 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4655  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9747 - loss: 0.0911 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4656  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9747 - loss: 0.0910 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4656  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9747 - loss: 0.0909 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4656  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9747 - loss: 0.0909 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4657  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9747 - loss: 0.0908 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4657  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9747 - loss: 0.0907 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4657  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9748 - loss: 0.0906 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4658  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9748 - loss: 0.0905 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4658  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9748 - loss: 0.0905 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4658  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9748 - loss: 0.0904 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4659  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9748 - loss: 0.0903 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4659  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9748 - loss: 0.0902 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4659  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9748 - loss: 0.0901 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4660  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9748 - loss: 0.0901 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4660  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9749 - loss: 0.0900 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4660  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9749 - loss: 0.0899 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4661  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9749 - loss: 0.0898 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4661  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9749 - loss: 0.0898 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4661  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9749 - loss: 0.0897 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4662  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9749 - loss: 0.0897 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4662  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9749 - loss: 0.0896 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4662  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9749 - loss: 0.0895 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4663  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9749 - loss: 0.0895 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4663  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9750 - loss: 0.0894 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4663  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9750 - loss: 0.0893 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4664  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9750 - loss: 0.0893 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4664  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9750 - loss: 0.0892 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4664  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9750 - loss: 0.0891 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9750 - loss: 0.0891 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9750 - loss: 0.0890 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9750 - loss: 0.0890 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4665  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9750 - loss: 0.0889 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4666  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9750 - loss: 0.0888 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4666  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9751 - loss: 0.0888 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4666  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9751 - loss: 0.0887 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4667  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9751 - loss: 0.0887 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4667  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9751 - loss: 0.0886 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4667  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9751 - loss: 0.0886 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9751 - loss: 0.0885 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9751 - loss: 0.0884 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9751 - loss: 0.0884 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4668  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9751 - loss: 0.0883 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4669  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9751 - loss: 0.0883 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4669  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9751 - loss: 0.0882 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4669   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9751 - loss: 0.0882 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4670  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9752 - loss: 0.0881 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4670  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9752 - loss: 0.0881 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4670  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9752 - loss: 0.0880 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4670  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9752 - loss: 0.0880 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9752 - loss: 0.0879 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9752 - loss: 0.0879 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4671  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9752 - loss: 0.0878 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9752 - loss: 0.0878 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9752 - loss: 0.0877 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9752 - loss: 0.0877 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4672  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9752 - loss: 0.0877 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9752 - loss: 0.0876 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9752 - loss: 0.0876 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9752 - loss: 0.0875 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4673 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9753 - loss: 0.0875 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4674 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9753 - loss: 0.0874 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4674 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9753 - loss: 0.0874 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4674 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9753 - loss: 0.0873 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9753 - loss: 0.0873 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9753 - loss: 0.0872 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9753 - loss: 0.0872 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4675 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9753 - loss: 0.0872 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9753 - loss: 0.0871 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9753 - loss: 0.0871 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9753 - loss: 0.0870 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4676 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9753 - loss: 0.0870 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9753 - loss: 0.0869 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9753 - loss: 0.0869 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9753 - loss: 0.0869 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4677 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9754 - loss: 0.0868 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4678 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9754 - loss: 0.0868 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4678 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9754 - loss: 0.0867 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4678 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9761 - loss: 0.0816 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4707 - val_accuracy: 0.9597 - val_loss: 0.1937 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4904 Epoch 3/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 0.9734 - loss: 0.0846 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4713  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9729 - loss: 0.0863 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4722  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9730 - loss: 0.0859 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4727  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9737 - loss: 0.0839 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4732  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9742 - loss: 0.0825 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4736  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 0.9746 - loss: 0.0813 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4740  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 0.9749 - loss: 0.0804 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4743  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 293ms/step - accuracy: 0.9752 - loss: 0.0794 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4745  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 293ms/step - accuracy: 0.9755 - loss: 0.0786 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4748  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 0.9757 - loss: 0.0781 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4750  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 0.9758 - loss: 0.0777 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4751  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 0.9759 - loss: 0.0774 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4751  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 0.9760 - loss: 0.0771 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4752  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 0.9760 - loss: 0.0770 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4752  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 0.9761 - loss: 0.0769 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4753  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 0.9761 - loss: 0.0768 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4753  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 0.9762 - loss: 0.0766 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4753  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 0.9762 - loss: 0.0765 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4754  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 0.9763 - loss: 0.0764 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4754  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 0.9763 - loss: 0.0762 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 0.9764 - loss: 0.0760 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 0.9764 - loss: 0.0759 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4755  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 0.9765 - loss: 0.0758 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 0.9765 - loss: 0.0757 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  25/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 0.9765 - loss: 0.0756 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 0.9765 - loss: 0.0755 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 0.9765 - loss: 0.0755 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 0.9766 - loss: 0.0754 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4756  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 0.9766 - loss: 0.0754 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 0.9766 - loss: 0.0753 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 0.9766 - loss: 0.0752 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 0.9766 - loss: 0.0752 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 0.9767 - loss: 0.0751 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4757  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 0.9767 - loss: 0.0750 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  35/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 0.9767 - loss: 0.0750 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 0.9767 - loss: 0.0749 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 0.9767 - loss: 0.0749 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 0.9767 - loss: 0.0749 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 0.9767 - loss: 0.0748 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4758  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 0.9767 - loss: 0.0748 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 0.9768 - loss: 0.0748 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9768 - loss: 0.0747 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9768 - loss: 0.0747 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9768 - loss: 0.0747 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9768 - loss: 0.0746 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 294ms/step - accuracy: 0.9768 - loss: 0.0746 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 294ms/step - accuracy: 0.9768 - loss: 0.0745 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 294ms/step - accuracy: 0.9768 - loss: 0.0745 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 294ms/step - accuracy: 0.9768 - loss: 0.0745 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 294ms/step - accuracy: 0.9768 - loss: 0.0745 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 294ms/step - accuracy: 0.9768 - loss: 0.0744 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 294ms/step - accuracy: 0.9769 - loss: 0.0744 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 0.9769 - loss: 0.0744 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4760  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 0.9769 - loss: 0.0743 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 0.9769 - loss: 0.0743 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 0.9769 - loss: 0.0743 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 0.9769 - loss: 0.0742 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 0.9769 - loss: 0.0742 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  59/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9769 - loss: 0.0742 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9769 - loss: 0.0741 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4761  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9769 - loss: 0.0741 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9769 - loss: 0.0741 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 0.9769 - loss: 0.0741 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 0.9769 - loss: 0.0740 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 0.9770 - loss: 0.0740 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9770 - loss: 0.0740 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9770 - loss: 0.0739 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9770 - loss: 0.0739 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9770 - loss: 0.0739 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4762  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9770 - loss: 0.0738 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9770 - loss: 0.0738 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9770 - loss: 0.0738 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 0.9770 - loss: 0.0738 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 0.9770 - loss: 0.0737 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 0.9770 - loss: 0.0737 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9770 - loss: 0.0737 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9770 - loss: 0.0736 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4763  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9770 - loss: 0.0736 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9771 - loss: 0.0736 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9771 - loss: 0.0736 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9771 - loss: 0.0735 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9771 - loss: 0.0735 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9771 - loss: 0.0735 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9771 - loss: 0.0734 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9771 - loss: 0.0734 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9771 - loss: 0.0734 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9771 - loss: 0.0733 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9771 - loss: 0.0733 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9771 - loss: 0.0733 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9771 - loss: 0.0733 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9771 - loss: 0.0732 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9771 - loss: 0.0732 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9772 - loss: 0.0732 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9772 - loss: 0.0731 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4765  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9772 - loss: 0.0731 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9772 - loss: 0.0731 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9772 - loss: 0.0731 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9772 - loss: 0.0730 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9772 - loss: 0.0730 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9772 - loss: 0.0730 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9772 - loss: 0.0729 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9772 - loss: 0.0729 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9772 - loss: 0.0729 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4766 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9772 - loss: 0.0729 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9772 - loss: 0.0728 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9772 - loss: 0.0728 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9772 - loss: 0.0728 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9772 - loss: 0.0728 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9773 - loss: 0.0727 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9773 - loss: 0.0727 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9773 - loss: 0.0727 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9773 - loss: 0.0726 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4767 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9773 - loss: 0.0726 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9773 - loss: 0.0726 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9773 - loss: 0.0726 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9773 - loss: 0.0725 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9773 - loss: 0.0725 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9773 - loss: 0.0725 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9773 - loss: 0.0725 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9773 - loss: 0.0724 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4768 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9780 - loss: 0.0694 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4781 - val_accuracy: 0.9588 - val_loss: 0.1606 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4897 Epoch 4/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 0.9742 - loss: 0.0777 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4749  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9740 - loss: 0.0780 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4759  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9742 - loss: 0.0776 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4764  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9749 - loss: 0.0757 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4770  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9754 - loss: 0.0745 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4776  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9758 - loss: 0.0734 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4780  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9761 - loss: 0.0726 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4783  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9764 - loss: 0.0717 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4786  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9767 - loss: 0.0709 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4789  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9769 - loss: 0.0703 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4791  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9771 - loss: 0.0699 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4792  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9772 - loss: 0.0695 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4793  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9773 - loss: 0.0693 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4794  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9774 - loss: 0.0691 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4795  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9774 - loss: 0.0690 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4795  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9774 - loss: 0.0689 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4795  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9775 - loss: 0.0687 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4796  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 0.9776 - loss: 0.0686 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4796  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 0.9776 - loss: 0.0684 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4796  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 0.9777 - loss: 0.0682 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 0.9777 - loss: 0.0681 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9778 - loss: 0.0679 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4797  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9778 - loss: 0.0678 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9779 - loss: 0.0677 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9779 - loss: 0.0676 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 0.9779 - loss: 0.0675 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 0.9779 - loss: 0.0675 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 0.9780 - loss: 0.0674 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9780 - loss: 0.0673 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9780 - loss: 0.0673 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9780 - loss: 0.0672 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9781 - loss: 0.0671 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4798  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9781 - loss: 0.0670 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9781 - loss: 0.0669 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9781 - loss: 0.0669 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 0.9782 - loss: 0.0668 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 0.9782 - loss: 0.0668 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 0.9782 - loss: 0.0667 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 0.9782 - loss: 0.0667 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 0.9782 - loss: 0.0666 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4799  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 0.9782 - loss: 0.0666 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9782 - loss: 0.0665 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9782 - loss: 0.0665 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9783 - loss: 0.0664 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 294ms/step - accuracy: 0.9783 - loss: 0.0664 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 294ms/step - accuracy: 0.9783 - loss: 0.0663 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 294ms/step - accuracy: 0.9783 - loss: 0.0663 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 294ms/step - accuracy: 0.9783 - loss: 0.0662 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 294ms/step - accuracy: 0.9783 - loss: 0.0662 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 294ms/step - accuracy: 0.9783 - loss: 0.0662 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 294ms/step - accuracy: 0.9783 - loss: 0.0661 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  52/120 ━━━━━━━━━━━━━━━━━━━━ 19s 294ms/step - accuracy: 0.9784 - loss: 0.0661 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 294ms/step - accuracy: 0.9784 - loss: 0.0661 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 294ms/step - accuracy: 0.9784 - loss: 0.0660 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 294ms/step - accuracy: 0.9784 - loss: 0.0660 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 294ms/step - accuracy: 0.9784 - loss: 0.0659 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 294ms/step - accuracy: 0.9784 - loss: 0.0659 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 294ms/step - accuracy: 0.9784 - loss: 0.0659 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  59/120 ━━━━━━━━━━━━━━━━━━━━ 17s 294ms/step - accuracy: 0.9784 - loss: 0.0658 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 294ms/step - accuracy: 0.9784 - loss: 0.0658 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 294ms/step - accuracy: 0.9785 - loss: 0.0657 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 294ms/step - accuracy: 0.9785 - loss: 0.0657 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 294ms/step - accuracy: 0.9785 - loss: 0.0657 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4801  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 294ms/step - accuracy: 0.9785 - loss: 0.0656 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 294ms/step - accuracy: 0.9785 - loss: 0.0656 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 294ms/step - accuracy: 0.9785 - loss: 0.0656 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 294ms/step - accuracy: 0.9785 - loss: 0.0655 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 294ms/step - accuracy: 0.9785 - loss: 0.0655 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9785 - loss: 0.0655 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9785 - loss: 0.0654 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9785 - loss: 0.0654 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9786 - loss: 0.0654 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 0.9786 - loss: 0.0653 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 0.9786 - loss: 0.0653 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 0.9786 - loss: 0.0653 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9786 - loss: 0.0652 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9786 - loss: 0.0652 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4802  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9786 - loss: 0.0652 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9786 - loss: 0.0651 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9786 - loss: 0.0651 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9786 - loss: 0.0651 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9786 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9786 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9786 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9787 - loss: 0.0650 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9787 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 0.9787 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 0.9787 - loss: 0.0649 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 0.9787 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 0.9787 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 0.9787 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 0.9787 - loss: 0.0648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9787 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9787 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9787 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9787 - loss: 0.0647 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9787 - loss: 0.0646 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9788 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9788 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9788 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9788 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9788 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9788 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9788 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9788 - loss: 0.0644 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9788 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9788 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9788 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9788 - loss: 0.0643 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4804 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9788 - loss: 0.0642 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9788 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9789 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9789 - loss: 0.0641 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9795 - loss: 0.0614 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4811 - val_accuracy: 0.9728 - val_loss: 0.0900 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4878 Epoch 5/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 0.9753 - loss: 0.0697 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4789  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 0.9752 - loss: 0.0700 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4793  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9756 - loss: 0.0693 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4796  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9763 - loss: 0.0676 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4800  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9768 - loss: 0.0663 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4803  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9772 - loss: 0.0653 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4805  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9775 - loss: 0.0645 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4807  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9778 - loss: 0.0636 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4808  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9781 - loss: 0.0629 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4810  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9783 - loss: 0.0623 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4812  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9785 - loss: 0.0620 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4813  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9786 - loss: 0.0616 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4814  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9787 - loss: 0.0614 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4814  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9788 - loss: 0.0613 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4815  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9788 - loss: 0.0612 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4815  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9789 - loss: 0.0610 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4815  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9790 - loss: 0.0609 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9790 - loss: 0.0607 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9791 - loss: 0.0606 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4816  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9791 - loss: 0.0604 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9792 - loss: 0.0603 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9792 - loss: 0.0601 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4817  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9793 - loss: 0.0600 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9793 - loss: 0.0599 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9794 - loss: 0.0598 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9794 - loss: 0.0598 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9794 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4818  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9794 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9795 - loss: 0.0596 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9795 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9795 - loss: 0.0595 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9795 - loss: 0.0594 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4819  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9796 - loss: 0.0593 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9796 - loss: 0.0592 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9796 - loss: 0.0591 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9796 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9797 - loss: 0.0590 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4820  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9797 - loss: 0.0589 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9797 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9797 - loss: 0.0588 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9797 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9798 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9798 - loss: 0.0586 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4821  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9798 - loss: 0.0585 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9798 - loss: 0.0585 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9798 - loss: 0.0584 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9798 - loss: 0.0584 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9798 - loss: 0.0584 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9799 - loss: 0.0583 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9799 - loss: 0.0583 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9799 - loss: 0.0583 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9799 - loss: 0.0582 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9799 - loss: 0.0582 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9799 - loss: 0.0582 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9799 - loss: 0.0581 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9799 - loss: 0.0581 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9799 - loss: 0.0581 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9799 - loss: 0.0580 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9800 - loss: 0.0580 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9800 - loss: 0.0580 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9800 - loss: 0.0580 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9800 - loss: 0.0579 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9800 - loss: 0.0579 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9800 - loss: 0.0579 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9800 - loss: 0.0579 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9800 - loss: 0.0578 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9800 - loss: 0.0578 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9800 - loss: 0.0578 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9800 - loss: 0.0578 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9800 - loss: 0.0577 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9800 - loss: 0.0577 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9800 - loss: 0.0577 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9801 - loss: 0.0577 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9801 - loss: 0.0577 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9801 - loss: 0.0576 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9801 - loss: 0.0576 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9801 - loss: 0.0576 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9801 - loss: 0.0576 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9801 - loss: 0.0575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9801 - loss: 0.0575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9801 - loss: 0.0575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9801 - loss: 0.0575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9801 - loss: 0.0575 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9801 - loss: 0.0574 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9801 - loss: 0.0574 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9801 - loss: 0.0574 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9801 - loss: 0.0574 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9801 - loss: 0.0574 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9801 - loss: 0.0573 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9802 - loss: 0.0573 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9802 - loss: 0.0573 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9802 - loss: 0.0573 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9802 - loss: 0.0573 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9802 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9802 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9802 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9802 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9802 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9802 - loss: 0.0571 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9802 - loss: 0.0571 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9802 - loss: 0.0571 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9802 - loss: 0.0571 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9802 - loss: 0.0571 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9802 - loss: 0.0570 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9802 - loss: 0.0570 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9802 - loss: 0.0570 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9802 - loss: 0.0570 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9802 - loss: 0.0570 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9803 - loss: 0.0569 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9803 - loss: 0.0569 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9803 - loss: 0.0569 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9803 - loss: 0.0569 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9803 - loss: 0.0569 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9803 - loss: 0.0568 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9803 - loss: 0.0568 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9803 - loss: 0.0568 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4826 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9809 - loss: 0.0544 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833 - val_accuracy: 0.9761 - val_loss: 0.0775 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4878 Epoch 6/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9778 - loss: 0.0587 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4824  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9774 - loss: 0.0598 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4822  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9775 - loss: 0.0597 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4823  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9782 - loss: 0.0583 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4825  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9786 - loss: 0.0572 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4828  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9790 - loss: 0.0564 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4830  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9793 - loss: 0.0557 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4831  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9796 - loss: 0.0550 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4833  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9799 - loss: 0.0543 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4834  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9801 - loss: 0.0538 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4835  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9802 - loss: 0.0535 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4836  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9804 - loss: 0.0532 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4837  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9805 - loss: 0.0530 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4837  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9805 - loss: 0.0529 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4838  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9806 - loss: 0.0528 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4838  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9806 - loss: 0.0527 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4838  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9807 - loss: 0.0526 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4838  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9807 - loss: 0.0525 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4839  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9808 - loss: 0.0524 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4839  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9808 - loss: 0.0523 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4839  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9809 - loss: 0.0521 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4839  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9809 - loss: 0.0520 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9810 - loss: 0.0520 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9810 - loss: 0.0519 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9810 - loss: 0.0518 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9810 - loss: 0.0518 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9811 - loss: 0.0518 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9811 - loss: 0.0517 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9811 - loss: 0.0517 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9811 - loss: 0.0516 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4840  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9811 - loss: 0.0516 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9812 - loss: 0.0515 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9812 - loss: 0.0515 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9812 - loss: 0.0514 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9812 - loss: 0.0514 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9812 - loss: 0.0514 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9812 - loss: 0.0514 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9813 - loss: 0.0513 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9813 - loss: 0.0513 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9813 - loss: 0.0513 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9813 - loss: 0.0513 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4841  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9813 - loss: 0.0513 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9813 - loss: 0.0513 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9813 - loss: 0.0512 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9813 - loss: 0.0512 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9813 - loss: 0.0512 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9813 - loss: 0.0512 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9813 - loss: 0.0512 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9814 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9814 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9814 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9814 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9814 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9814 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9814 - loss: 0.0511 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9814 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9814 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4842  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9814 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9814 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9814 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9814 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9814 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9814 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9814 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9815 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9815 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9815 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9815 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9815 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9815 - loss: 0.0508 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9815 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9815 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4843  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9815 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9815 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9815 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9815 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9816 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9816 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9816 - loss: 0.0505 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9816 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9816 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9816 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9816 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9816 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4844 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9816 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9817 - loss: 0.0504 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9817 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9817 - loss: 0.0502 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9817 - loss: 0.0502 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9817 - loss: 0.0502 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9817 - loss: 0.0502 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9817 - loss: 0.0502 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4845 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9822 - loss: 0.0486 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850 - val_accuracy: 0.9768 - val_loss: 0.0768 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4906 Epoch 7/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9788 - loss: 0.0550 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4848  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9785 - loss: 0.0559 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4849  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9787 - loss: 0.0555 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9793 - loss: 0.0542 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4853  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9798 - loss: 0.0530 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4854  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9801 - loss: 0.0522 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9804 - loss: 0.0516 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4856  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9807 - loss: 0.0509 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4857  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9809 - loss: 0.0503 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9811 - loss: 0.0499 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9813 - loss: 0.0496 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9814 - loss: 0.0493 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9815 - loss: 0.0491 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9815 - loss: 0.0490 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9815 - loss: 0.0489 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9816 - loss: 0.0488 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9817 - loss: 0.0487 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9817 - loss: 0.0485 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9817 - loss: 0.0484 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9818 - loss: 0.0483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9818 - loss: 0.0482 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9819 - loss: 0.0481 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9819 - loss: 0.0480 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9820 - loss: 0.0479 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9820 - loss: 0.0479 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9820 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9820 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9820 - loss: 0.0478 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9820 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9821 - loss: 0.0477 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9821 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9821 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9822 - loss: 0.0475 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9822 - loss: 0.0474 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9822 - loss: 0.0473 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9823 - loss: 0.0472 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9823 - loss: 0.0471 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9823 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9823 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9823 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9823 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9824 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9824 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9824 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9824 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9824 - loss: 0.0469 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9824 - loss: 0.0468 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9824 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9824 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9824 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9824 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9824 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9824 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9824 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9825 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9825 - loss: 0.0467 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9825 - loss: 0.0466 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9825 - loss: 0.0465 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9825 - loss: 0.0464 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9825 - loss: 0.0464 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9825 - loss: 0.0464 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9825 - loss: 0.0464 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4859 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9829 - loss: 0.0451 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4861 - val_accuracy: 0.9773 - val_loss: 0.0698 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4876 Epoch 8/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 0.9803 - loss: 0.0507 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4851  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 0.9800 - loss: 0.0510 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9800 - loss: 0.0506 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4850  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9805 - loss: 0.0494 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4852  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9809 - loss: 0.0483 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4855  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9812 - loss: 0.0476 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4856  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9815 - loss: 0.0470 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9818 - loss: 0.0464 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4860  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9820 - loss: 0.0458 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4861  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9821 - loss: 0.0454 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4862  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9823 - loss: 0.0452 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4863  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9824 - loss: 0.0450 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4863  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9824 - loss: 0.0448 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9825 - loss: 0.0447 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 0.9825 - loss: 0.0447 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 0.9825 - loss: 0.0446 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 0.9826 - loss: 0.0445 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 0.9826 - loss: 0.0444 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 0.9827 - loss: 0.0443 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 0.9827 - loss: 0.0442 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 0.9828 - loss: 0.0441 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9828 - loss: 0.0440 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9828 - loss: 0.0439 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4865  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9829 - loss: 0.0439 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 0.9829 - loss: 0.0438 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 0.9829 - loss: 0.0438 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 0.9829 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 0.9829 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9829 - loss: 0.0437 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9830 - loss: 0.0436 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9830 - loss: 0.0436 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9830 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9830 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9830 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9831 - loss: 0.0434 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9831 - loss: 0.0433 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9831 - loss: 0.0432 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9832 - loss: 0.0431 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9832 - loss: 0.0430 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9832 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9833 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9833 - loss: 0.0429 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9833 - loss: 0.0428 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9833 - loss: 0.0427 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9833 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9833 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9834 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9834 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9834 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9834 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9834 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9834 - loss: 0.0426 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9834 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9834 - loss: 0.0424 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4868  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9834 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9835 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9835 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9835 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9835 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9835 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9835 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9835 - loss: 0.0423 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9835 - loss: 0.0422 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9835 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9835 - loss: 0.0420 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9836 - loss: 0.0420 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9836 - loss: 0.0420 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9836 - loss: 0.0420 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9841 - loss: 0.0404 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4873 - val_accuracy: 0.9768 - val_loss: 0.0694 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4873 Epoch 9/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 0.9819 - loss: 0.0453 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4857  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9817 - loss: 0.0454 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4858  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9819 - loss: 0.0448 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4860  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9824 - loss: 0.0435 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9828 - loss: 0.0425 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4867  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9831 - loss: 0.0418 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9833 - loss: 0.0413 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4871  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9835 - loss: 0.0408 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9837 - loss: 0.0403 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4874  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9838 - loss: 0.0400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9839 - loss: 0.0398 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9840 - loss: 0.0396 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9841 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4876  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9841 - loss: 0.0394 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9841 - loss: 0.0393 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9842 - loss: 0.0392 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9842 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9843 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9843 - loss: 0.0390 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9843 - loss: 0.0389 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9844 - loss: 0.0388 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9844 - loss: 0.0387 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9844 - loss: 0.0387 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9844 - loss: 0.0386 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9845 - loss: 0.0385 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9845 - loss: 0.0385 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9845 - loss: 0.0385 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9845 - loss: 0.0384 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9845 - loss: 0.0384 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9845 - loss: 0.0384 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9845 - loss: 0.0383 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9846 - loss: 0.0383 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9846 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9846 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9846 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9846 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4879  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9846 - loss: 0.0381 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9846 - loss: 0.0381 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9846 - loss: 0.0381 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9846 - loss: 0.0381 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9846 - loss: 0.0381 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9846 - loss: 0.0380 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9847 - loss: 0.0380 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9847 - loss: 0.0380 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9847 - loss: 0.0380 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9847 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9847 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9847 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9847 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9847 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9847 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9847 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9847 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9847 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9847 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9847 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9847 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9847 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9848 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9848 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9848 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9849 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9849 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9849 - loss: 0.0374 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9849 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9849 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9849 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9849 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9849 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9852 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885 - val_accuracy: 0.9751 - val_loss: 0.0796 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4886 Epoch 10/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9828 - loss: 0.0417 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9824 - loss: 0.0421 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9825 - loss: 0.0417 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4866  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9829 - loss: 0.0408 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4869  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9833 - loss: 0.0400 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4872  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9835 - loss: 0.0395 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4875  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9837 - loss: 0.0390 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4877  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9840 - loss: 0.0386 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4878  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9841 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4880  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9843 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9844 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4882  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9845 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4883  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9845 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4883  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9846 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4883  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9846 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4883  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9847 - loss: 0.0371 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4883  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9847 - loss: 0.0370 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4884  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9847 - loss: 0.0369 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4884  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9848 - loss: 0.0368 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4884  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9848 - loss: 0.0367 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4884  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9849 - loss: 0.0366 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9849 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9849 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9850 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9850 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9850 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9851 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9851 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9851 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9851 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9851 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9852 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9852 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9852 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9852 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9853 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9853 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9853 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9853 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9853 - loss: 0.0356 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9853 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9853 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9854 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9854 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9854 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9854 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9854 - loss: 0.0353 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9854 - loss: 0.0353 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9854 - loss: 0.0353 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9855 - loss: 0.0352 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9855 - loss: 0.0352 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9855 - loss: 0.0352 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9855 - loss: 0.0351 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9855 - loss: 0.0351 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9855 - loss: 0.0351 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9855 - loss: 0.0351 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9855 - loss: 0.0350 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9856 - loss: 0.0350 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9856 - loss: 0.0350 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9856 - loss: 0.0349 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9856 - loss: 0.0349 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9856 - loss: 0.0349 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9856 - loss: 0.0349 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9856 - loss: 0.0349 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9856 - loss: 0.0348 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9856 - loss: 0.0348 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9856 - loss: 0.0348 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9856 - loss: 0.0348 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9857 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9857 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9857 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9857 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9857 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9857 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9857 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9857 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9857 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9857 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9857 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9857 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9857 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9857 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9857 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9858 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9858 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9858 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9858 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9858 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9858 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9859 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9859 - loss: 0.0342 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9859 - loss: 0.0342 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9859 - loss: 0.0342 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9861 - loss: 0.0336 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893 - val_accuracy: 0.9764 - val_loss: 0.0747 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4901 Epoch 11/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9843 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4884  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9840 - loss: 0.0384 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9841 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4881  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9845 - loss: 0.0370 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4883  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9848 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4885  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9850 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4886  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9852 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4887  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9854 - loss: 0.0350 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9856 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9857 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9857 - loss: 0.0342 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9858 - loss: 0.0340 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9859 - loss: 0.0339 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4892  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9859 - loss: 0.0338 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4892  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9859 - loss: 0.0338 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4892  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9860 - loss: 0.0337 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4892  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9860 - loss: 0.0336 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9860 - loss: 0.0336 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9861 - loss: 0.0335 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9861 - loss: 0.0334 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9861 - loss: 0.0333 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9862 - loss: 0.0332 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9862 - loss: 0.0332 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9862 - loss: 0.0331 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9862 - loss: 0.0331 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9863 - loss: 0.0330 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9863 - loss: 0.0330 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9863 - loss: 0.0329 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9863 - loss: 0.0329 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9863 - loss: 0.0329 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9863 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9864 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9866 - loss: 0.0323 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9866 - loss: 0.0323 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9866 - loss: 0.0323 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9866 - loss: 0.0322 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9866 - loss: 0.0322 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9866 - loss: 0.0322 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9866 - loss: 0.0322 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9866 - loss: 0.0322 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9866 - loss: 0.0321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9866 - loss: 0.0321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9866 - loss: 0.0321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9866 - loss: 0.0321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9867 - loss: 0.0321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9867 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9867 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9867 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9868 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9868 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9870 - loss: 0.0311 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4901 - val_accuracy: 0.9754 - val_loss: 0.0856 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4908 Epoch 12/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.9843 - loss: 0.0369 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4892  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9840 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9842 - loss: 0.0371 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9847 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9850 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9852 - loss: 0.0349 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4894  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9854 - loss: 0.0346 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9856 - loss: 0.0342 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9857 - loss: 0.0339 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9858 - loss: 0.0337 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9859 - loss: 0.0335 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9860 - loss: 0.0334 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9860 - loss: 0.0333 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9860 - loss: 0.0332 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 0.9861 - loss: 0.0332 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 0.9861 - loss: 0.0332 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9861 - loss: 0.0331 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9861 - loss: 0.0331 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9862 - loss: 0.0330 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9862 - loss: 0.0330 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9862 - loss: 0.0329 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9862 - loss: 0.0329 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9862 - loss: 0.0329 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9863 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9863 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9863 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9863 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9863 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9863 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9863 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9863 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9863 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9863 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9863 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 0.9863 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9863 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9864 - loss: 0.0327 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9864 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9865 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9865 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4900 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4900 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9870 - loss: 0.0313 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902 - val_accuracy: 0.9751 - val_loss: 0.0791 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4891 Epoch 13/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.9859 - loss: 0.0339 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9857 - loss: 0.0342 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4889  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9858 - loss: 0.0338 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4888  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9862 - loss: 0.0330 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4890  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9865 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4891  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9867 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4892  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9869 - loss: 0.0315 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4893  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9871 - loss: 0.0311 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4895  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9872 - loss: 0.0308 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4896  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9873 - loss: 0.0306 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4897  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9874 - loss: 0.0304 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9874 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9875 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9875 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4900  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9875 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4900  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9875 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4901  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9875 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4901  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9876 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4901  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9876 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9876 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9876 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9877 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9877 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9877 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9877 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9877 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9877 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9877 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9877 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9877 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9877 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9877 - loss: 0.0294 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9878 - loss: 0.0294 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9878 - loss: 0.0294 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9878 - loss: 0.0294 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9878 - loss: 0.0294 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9878 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9878 - loss: 0.0292 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9878 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9878 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9878 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9878 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9879 - loss: 0.0291 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9879 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9879 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9879 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9880 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9880 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9880 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9880 - loss: 0.0288 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9881 - loss: 0.0284 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4911 - val_accuracy: 0.9755 - val_loss: 0.1123 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4938 Epoch 14/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9850 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9849 - loss: 0.0348 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9851 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4898  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9855 - loss: 0.0337 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9857 - loss: 0.0331 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4900  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9859 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4900  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9861 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4901  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9862 - loss: 0.0321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4901  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9864 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9865 - loss: 0.0316 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9865 - loss: 0.0315 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9866 - loss: 0.0314 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9867 - loss: 0.0313 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9867 - loss: 0.0313 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4903  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9867 - loss: 0.0312 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9867 - loss: 0.0312 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9868 - loss: 0.0311 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9868 - loss: 0.0311 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9868 - loss: 0.0310 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9869 - loss: 0.0309 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9869 - loss: 0.0309 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9869 - loss: 0.0308 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9869 - loss: 0.0308 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9869 - loss: 0.0308 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9870 - loss: 0.0307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9870 - loss: 0.0307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9870 - loss: 0.0307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9870 - loss: 0.0307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9870 - loss: 0.0307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9870 - loss: 0.0306 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9870 - loss: 0.0306 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9871 - loss: 0.0306 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9871 - loss: 0.0306 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9871 - loss: 0.0305 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9871 - loss: 0.0305 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9871 - loss: 0.0305 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9871 - loss: 0.0305 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9871 - loss: 0.0305 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9871 - loss: 0.0305 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9871 - loss: 0.0304 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9871 - loss: 0.0304 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9871 - loss: 0.0304 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9871 - loss: 0.0304 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9872 - loss: 0.0304 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9872 - loss: 0.0303 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9872 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9872 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9872 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9872 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9872 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9873 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9873 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9873 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9873 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9873 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9873 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9873 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9873 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9873 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9873 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9873 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9874 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9874 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9874 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9874 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9874 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9874 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9874 - loss: 0.0299 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9874 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9874 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9874 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9874 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9874 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9875 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9875 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9875 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9875 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9875 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9875 - loss: 0.0297 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9875 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9875 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9875 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9875 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9875 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9875 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9875 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9875 - loss: 0.0295 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9879 - loss: 0.0286 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4910 - val_accuracy: 0.9766 - val_loss: 0.0947 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4932 Epoch 15/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9872 - loss: 0.0302 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4901  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9865 - loss: 0.0316 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4899  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9866 - loss: 0.0315 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4900  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9869 - loss: 0.0307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4902  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9872 - loss: 0.0301 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4904  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9874 - loss: 0.0296 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4905  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9876 - loss: 0.0293 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4906  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9877 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4907  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9879 - loss: 0.0287 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4908  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9880 - loss: 0.0284 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4909  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9881 - loss: 0.0283 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4909  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9881 - loss: 0.0281 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4909  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9882 - loss: 0.0280 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4910  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9882 - loss: 0.0279 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4910  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9883 - loss: 0.0279 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4910  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9883 - loss: 0.0278 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4910  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9883 - loss: 0.0277 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4911  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9884 - loss: 0.0276 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4911  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9884 - loss: 0.0276 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4911  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9884 - loss: 0.0275 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4911  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9885 - loss: 0.0274 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9885 - loss: 0.0273 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9885 - loss: 0.0273 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9886 - loss: 0.0272 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9886 - loss: 0.0272 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9886 - loss: 0.0272 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9886 - loss: 0.0271 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9886 - loss: 0.0271 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4912  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9886 - loss: 0.0271 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9887 - loss: 0.0270 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9887 - loss: 0.0270 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9887 - loss: 0.0270 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9887 - loss: 0.0269 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9887 - loss: 0.0269 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9887 - loss: 0.0269 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9887 - loss: 0.0268 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9888 - loss: 0.0268 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9888 - loss: 0.0268 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9888 - loss: 0.0268 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9888 - loss: 0.0267 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9888 - loss: 0.0267 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9888 - loss: 0.0267 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9888 - loss: 0.0267 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9888 - loss: 0.0266 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9888 - loss: 0.0266 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9889 - loss: 0.0266 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9889 - loss: 0.0265 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9889 - loss: 0.0265 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9889 - loss: 0.0265 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9889 - loss: 0.0265 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9889 - loss: 0.0265 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9889 - loss: 0.0264 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9889 - loss: 0.0264 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9889 - loss: 0.0264 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9890 - loss: 0.0264 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9890 - loss: 0.0263 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9890 - loss: 0.0263 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9890 - loss: 0.0263 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9890 - loss: 0.0263 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9890 - loss: 0.0262 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9890 - loss: 0.0262 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9890 - loss: 0.0262 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9890 - loss: 0.0262 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9890 - loss: 0.0261 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9891 - loss: 0.0261 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9891 - loss: 0.0261 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9891 - loss: 0.0261 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9891 - loss: 0.0260 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9891 - loss: 0.0260 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9891 - loss: 0.0260 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9891 - loss: 0.0260 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9891 - loss: 0.0259 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9891 - loss: 0.0259 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9892 - loss: 0.0259 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9892 - loss: 0.0259 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4916  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9892 - loss: 0.0258 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9892 - loss: 0.0258 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9892 - loss: 0.0258 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9892 - loss: 0.0258 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9892 - loss: 0.0258 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9892 - loss: 0.0257 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9892 - loss: 0.0257 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9892 - loss: 0.0257 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9892 - loss: 0.0257 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9893 - loss: 0.0256 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9893 - loss: 0.0256 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9893 - loss: 0.0256 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9893 - loss: 0.0256 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9893 - loss: 0.0256 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9893 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9893 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9893 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9893 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9893 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9894 - loss: 0.0254 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9894 - loss: 0.0254 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9894 - loss: 0.0254 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9894 - loss: 0.0254 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9894 - loss: 0.0254 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9894 - loss: 0.0253 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9894 - loss: 0.0253 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9894 - loss: 0.0253 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9894 - loss: 0.0253 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9894 - loss: 0.0253 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9894 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9894 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9894 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9895 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9895 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9895 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9895 - loss: 0.0251 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9895 - loss: 0.0251 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9895 - loss: 0.0251 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9895 - loss: 0.0251 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9895 - loss: 0.0251 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9895 - loss: 0.0251 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9895 - loss: 0.0250 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9895 - loss: 0.0250 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9895 - loss: 0.0250 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9895 - loss: 0.0250 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9903 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925 - val_accuracy: 0.9766 - val_loss: 0.0924 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4930 Epoch 16/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9891 - loss: 0.0269 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4910  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9889 - loss: 0.0270 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4909  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9890 - loss: 0.0267 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4909  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9892 - loss: 0.0261 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4911  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9893 - loss: 0.0257 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4913  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9894 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4914  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9894 - loss: 0.0253 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4915  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9895 - loss: 0.0251 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4917  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9896 - loss: 0.0249 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9897 - loss: 0.0247 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4918  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9897 - loss: 0.0246 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9897 - loss: 0.0245 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4919  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9898 - loss: 0.0245 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4920  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9898 - loss: 0.0245 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4920  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9898 - loss: 0.0244 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4920  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9898 - loss: 0.0244 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4920  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9898 - loss: 0.0243 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4920  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9898 - loss: 0.0243 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4921  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9898 - loss: 0.0243 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4921  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9899 - loss: 0.0242 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4921  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9899 - loss: 0.0242 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4921  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9899 - loss: 0.0241 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4921  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9899 - loss: 0.0241 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4921  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9899 - loss: 0.0241 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9899 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9899 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9899 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9900 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9900 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9900 - loss: 0.0239 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9900 - loss: 0.0239 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9900 - loss: 0.0239 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9900 - loss: 0.0239 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9900 - loss: 0.0238 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9900 - loss: 0.0238 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9900 - loss: 0.0238 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9900 - loss: 0.0238 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9900 - loss: 0.0238 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9901 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9901 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9901 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9901 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9901 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9901 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9901 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9901 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9901 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9901 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9901 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9902 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9902 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9902 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9902 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9902 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9902 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9902 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9902 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9902 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9902 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9902 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9902 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9902 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9902 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9903 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9903 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9903 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9903 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9903 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9903 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9903 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9903 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9904 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9904 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9904 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9904 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9905 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9905 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9905 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9906 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9906 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9906 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9911 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931 - val_accuracy: 0.9764 - val_loss: 0.1048 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4944 Epoch 17/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9900 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9897 - loss: 0.0242 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9898 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9901 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9903 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9904 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9905 - loss: 0.0223 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9906 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9907 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9908 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9908 - loss: 0.0217 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9909 - loss: 0.0216 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9909 - loss: 0.0216 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9909 - loss: 0.0216 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9909 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9910 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9910 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9910 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9910 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9910 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9910 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9910 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9911 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9911 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9911 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9911 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9911 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9911 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9912 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9912 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9912 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9912 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9915 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9915 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9915 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9915 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9918 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 - val_accuracy: 0.9760 - val_loss: 0.1016 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4935 Epoch 18/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9889 - loss: 0.0260 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9886 - loss: 0.0265 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4923  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9888 - loss: 0.0263 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4922  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9891 - loss: 0.0256 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4924  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9893 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9894 - loss: 0.0248 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9895 - loss: 0.0246 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9896 - loss: 0.0244 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9898 - loss: 0.0241 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9898 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9899 - loss: 0.0239 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9900 - loss: 0.0238 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9900 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9900 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9900 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9901 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9901 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9901 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9901 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9902 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9902 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9902 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9903 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9903 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9903 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9903 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9903 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9904 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9904 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9904 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9904 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9905 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9905 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9905 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9905 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9906 - loss: 0.0225 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9906 - loss: 0.0225 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9906 - loss: 0.0225 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9906 - loss: 0.0224 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9906 - loss: 0.0224 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9906 - loss: 0.0224 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9906 - loss: 0.0224 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9906 - loss: 0.0223 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9907 - loss: 0.0223 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9907 - loss: 0.0223 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9907 - loss: 0.0223 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9907 - loss: 0.0222 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9907 - loss: 0.0222 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9907 - loss: 0.0222 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9907 - loss: 0.0222 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9907 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9907 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9907 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9908 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9908 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9908 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9908 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9909 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9909 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9909 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9909 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9910 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933 - val_accuracy: 0.9761 - val_loss: 0.1012 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4937 Epoch 19/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9905 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 0.9903 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9903 - loss: 0.0232 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4925  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9905 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4926  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9906 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4927  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9907 - loss: 0.0225 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9907 - loss: 0.0223 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9908 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9909 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9910 - loss: 0.0217 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 0.9910 - loss: 0.0216 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9910 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9911 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9911 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 0.9911 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9911 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9911 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9912 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9912 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9912 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9912 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9913 - loss: 0.0210 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9913 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9913 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9914 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9914 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9914 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9915 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9915 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9915 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9915 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9916 - loss: 0.0203 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9916 - loss: 0.0203 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9916 - loss: 0.0203 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9916 - loss: 0.0203 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9916 - loss: 0.0203 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9916 - loss: 0.0202 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9916 - loss: 0.0202 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9916 - loss: 0.0202 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9916 - loss: 0.0202 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9916 - loss: 0.0202 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9916 - loss: 0.0201 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9917 - loss: 0.0201 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9917 - loss: 0.0201 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9917 - loss: 0.0201 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9917 - loss: 0.0201 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9917 - loss: 0.0200 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9917 - loss: 0.0200 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9917 - loss: 0.0200 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9917 - loss: 0.0200 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9917 - loss: 0.0200 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9917 - loss: 0.0199 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9917 - loss: 0.0199 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9917 - loss: 0.0199 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9918 - loss: 0.0199 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9918 - loss: 0.0199 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9918 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9918 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9918 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9918 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9918 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9918 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9918 - loss: 0.0197 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9918 - loss: 0.0197 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9918 - loss: 0.0197 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9918 - loss: 0.0197 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9919 - loss: 0.0197 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9919 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9919 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9919 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9919 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9919 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9919 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9919 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9919 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9919 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9919 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9919 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9919 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9919 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 - val_accuracy: 0.9762 - val_loss: 0.1236 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4957 Epoch 20/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9923 - loss: 0.0187 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9921 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9922 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9922 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9922 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9922 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9922 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9922 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9922 - loss: 0.0190 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9922 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9922 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9922 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9921 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9921 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9920 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9920 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9920 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9920 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9921 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9921 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9921 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9921 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9921 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9921 - loss: 0.0190 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9921 - loss: 0.0190 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9921 - loss: 0.0190 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9922 - loss: 0.0190 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9922 - loss: 0.0190 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9922 - loss: 0.0189 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9922 - loss: 0.0189 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9922 - loss: 0.0189 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9922 - loss: 0.0189 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9922 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9922 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9922 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9922 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9923 - loss: 0.0187 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9923 - loss: 0.0187 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9923 - loss: 0.0187 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9923 - loss: 0.0187 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9923 - loss: 0.0186 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9923 - loss: 0.0186 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9923 - loss: 0.0186 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9923 - loss: 0.0186 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9923 - loss: 0.0186 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9923 - loss: 0.0185 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9924 - loss: 0.0185 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9924 - loss: 0.0185 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9924 - loss: 0.0185 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9924 - loss: 0.0185 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9924 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9924 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9924 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9924 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9924 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9924 - loss: 0.0183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9924 - loss: 0.0183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9924 - loss: 0.0183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9925 - loss: 0.0183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9925 - loss: 0.0183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9925 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9925 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9925 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9925 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9925 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9926 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9926 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9926 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9926 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9926 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9926 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9926 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9926 - loss: 0.0178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9933 - loss: 0.0164 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948 - val_accuracy: 0.9760 - val_loss: 0.1136 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4949 Epoch 21/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9908 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9909 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9911 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9915 - loss: 0.0203 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9918 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9921 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9922 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9923 - loss: 0.0186 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9924 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9924 - loss: 0.0183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9924 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9925 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9925 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9925 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9925 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9925 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9925 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9926 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9926 - loss: 0.0178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9926 - loss: 0.0178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9926 - loss: 0.0178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9926 - loss: 0.0178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9926 - loss: 0.0177 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9926 - loss: 0.0177 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9927 - loss: 0.0177 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9927 - loss: 0.0177 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9927 - loss: 0.0177 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9927 - loss: 0.0176 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9927 - loss: 0.0176 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9927 - loss: 0.0176 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9927 - loss: 0.0176 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9927 - loss: 0.0175 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9927 - loss: 0.0175 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9928 - loss: 0.0175 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9928 - loss: 0.0174 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9928 - loss: 0.0174 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9928 - loss: 0.0174 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9928 - loss: 0.0174 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9928 - loss: 0.0173 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9928 - loss: 0.0173 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9929 - loss: 0.0173 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9929 - loss: 0.0172 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9929 - loss: 0.0172 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9929 - loss: 0.0172 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9929 - loss: 0.0171 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9929 - loss: 0.0171 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9929 - loss: 0.0171 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9930 - loss: 0.0170 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9930 - loss: 0.0170 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9930 - loss: 0.0170 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9930 - loss: 0.0169 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9930 - loss: 0.0169 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9930 - loss: 0.0169 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9930 - loss: 0.0168 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9931 - loss: 0.0168 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9931 - loss: 0.0168 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9931 - loss: 0.0168 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9931 - loss: 0.0167 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9931 - loss: 0.0167 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9931 - loss: 0.0167 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9931 - loss: 0.0166 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9931 - loss: 0.0166 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9932 - loss: 0.0166 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9932 - loss: 0.0166 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9932 - loss: 0.0165 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9932 - loss: 0.0165 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9932 - loss: 0.0165 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9932 - loss: 0.0164 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9932 - loss: 0.0164 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9932 - loss: 0.0164 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9933 - loss: 0.0164 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9933 - loss: 0.0163 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9933 - loss: 0.0163 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9933 - loss: 0.0163 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9933 - loss: 0.0163 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9933 - loss: 0.0162 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9933 - loss: 0.0162 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9933 - loss: 0.0162 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9933 - loss: 0.0161 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9934 - loss: 0.0161 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9934 - loss: 0.0161 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9934 - loss: 0.0161 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9934 - loss: 0.0160 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9934 - loss: 0.0160 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9934 - loss: 0.0160 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9934 - loss: 0.0160 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9934 - loss: 0.0160 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9934 - loss: 0.0159 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9935 - loss: 0.0159 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9935 - loss: 0.0159 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9935 - loss: 0.0159 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9935 - loss: 0.0158 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9935 - loss: 0.0158 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9935 - loss: 0.0158 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9935 - loss: 0.0158 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9935 - loss: 0.0158 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9935 - loss: 0.0157 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9935 - loss: 0.0157 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9936 - loss: 0.0157 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9936 - loss: 0.0157 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9936 - loss: 0.0157 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9936 - loss: 0.0156 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9936 - loss: 0.0156 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9936 - loss: 0.0156 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9936 - loss: 0.0156 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9936 - loss: 0.0156 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9936 - loss: 0.0155 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9936 - loss: 0.0155 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9936 - loss: 0.0155 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9936 - loss: 0.0155 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9936 - loss: 0.0155 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9937 - loss: 0.0155 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9937 - loss: 0.0154 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9937 - loss: 0.0154 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9937 - loss: 0.0154 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9937 - loss: 0.0154 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9937 - loss: 0.0154 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9945 - loss: 0.0135 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4956 - val_accuracy: 0.9764 - val_loss: 0.1159 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4952 Epoch 22/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9943 - loss: 0.0141 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4955  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9942 - loss: 0.0144 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4954  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9942 - loss: 0.0143 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4954  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9944 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4955  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9945 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4956  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9946 - loss: 0.0134 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4956  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9947 - loss: 0.0132 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4957  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9948 - loss: 0.0130 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4957  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9948 - loss: 0.0129 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4958  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9949 - loss: 0.0128 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4958  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9949 - loss: 0.0127 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4958  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9949 - loss: 0.0126 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4958  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9950 - loss: 0.0126 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9950 - loss: 0.0125 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9950 - loss: 0.0125 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9950 - loss: 0.0124 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9950 - loss: 0.0124 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9950 - loss: 0.0124 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9951 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9951 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9951 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9951 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4959  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9951 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9951 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9951 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9951 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9951 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9951 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9951 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9952 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9952 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9952 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9952 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9952 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9952 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9952 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9952 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9952 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9952 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9952 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9952 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9952 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9952 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9953 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9953 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9953 - loss: 0.0119 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9953 - loss: 0.0118 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9953 - loss: 0.0117 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9953 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9954 - loss: 0.0116 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9954 - loss: 0.0115 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9954 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9954 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9954 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9954 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9955 - loss: 0.0114 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4963 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4963 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4963 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4963 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9955 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4963 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9959 - loss: 0.0104 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965 - val_accuracy: 0.9761 - val_loss: 0.1381 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4965 Epoch 23/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 0.9960 - loss: 0.0104 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9957 - loss: 0.0112 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4962  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 0.9957 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4963  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9958 - loss: 0.0111 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9959 - loss: 0.0106 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9960 - loss: 0.0104 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9961 - loss: 0.0102 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9961 - loss: 0.0101 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9961 - loss: 0.0100 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9962 - loss: 0.0100 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9962 - loss: 0.0099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9962 - loss: 0.0099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9963 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9963 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9963 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9964 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9964 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9965 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 - val_accuracy: 0.9756 - val_loss: 0.1620 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4969 Epoch 24/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9958 - loss: 0.0111 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9957 - loss: 0.0113 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9958 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9960 - loss: 0.0105 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9961 - loss: 0.0102 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9962 - loss: 0.0099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9962 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9963 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9963 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9963 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9963 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9963 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9963 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9963 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0091 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9968 - loss: 0.0082 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 - val_accuracy: 0.9768 - val_loss: 0.1396 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4967 Epoch 25/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9979 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9980 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9980 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9980 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9979 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9980 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9980 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9981 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982 - val_accuracy: 0.9765 - val_loss: 0.1605 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4971 Epoch 26/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9990 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9990 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9990 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9990 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9990 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9990 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9990 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9989 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9990 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9990 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9991 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9991 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 - val_accuracy: 0.9771 - val_loss: 0.1718 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4977 Epoch 27/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9999 - loss: 7.3340e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9999 - loss: 7.7795e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9999 - loss: 8.0783e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9999 - loss: 8.8098e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9998 - loss: 9.8306e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9998 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994   7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9997 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9997 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9997 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9997 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9997 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9996 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9996 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9993 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9993 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9993 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 - val_accuracy: 0.9765 - val_loss: 0.1867 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4976 Epoch 28/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9991 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 - val_accuracy: 0.9760 - val_loss: 0.1825 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4974 Epoch 29/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9992 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9992 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9993 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9993 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 6.7348e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 - val_accuracy: 0.9772 - val_loss: 0.1959 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4981 Epoch 30/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.3154e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.3773e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.3808e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.3550e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.3305e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.3107e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.2968e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.2840e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.2711e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.2614e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.2558e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.2498e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.2455e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.2434e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.2420e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.2395e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.2367e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2339e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.2310e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.2280e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.2247e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.2216e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.2193e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2168e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2146e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2130e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2116e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2099e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2079e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2064e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2048e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2032e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2014e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1997e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1983e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1968e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1954e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1943e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1932e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1920e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1907e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1894e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1881e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1868e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1855e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1843e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1832e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1821e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1811e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1801e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1792e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1783e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1773e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1763e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1753e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1743e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1733e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1722e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1712e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1703e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1693e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1685e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1676e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1668e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1659e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1650e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1640e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1631e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1622e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1612e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1603e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1594e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1585e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1576e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1568e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1559e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1550e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1541e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1532e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1523e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1514e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1505e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1496e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1487e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1478e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1470e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1462e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1453e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1445e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1436e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1427e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1419e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1410e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1401e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1392e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1383e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1375e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1367e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1358e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1350e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1341e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1333e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1324e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1316e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1307e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1298e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1290e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1281e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1273e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1265e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1256e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1248e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1240e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1232e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1223e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1215e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1207e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1198e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1190e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1182e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0200e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9772 - val_loss: 0.2054 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4982 Epoch 31/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.3714e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.6600e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.6469e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.4753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.2914e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.1684e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.0763e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.0158e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.9463e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.8930e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.8632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.8304e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.8090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.7983e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.7902e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.7741e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.7558e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.7381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.7205e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.7023e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.6829e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6658e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.6532e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.6391e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.6276e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.6191e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.6113e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.6017e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.5908e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.5803e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.5696e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.5592e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.5477e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.5369e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.5281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.5192e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.5111e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.5043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.4977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.4900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.4818e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.4733e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.4648e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.4564e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.4475e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.4390e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.4316e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.4240e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.4169e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4106e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4046e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.3980e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.3913e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.3847e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.3780e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.3711e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3639e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3570e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3506e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3440e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3378e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3321e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3267e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3211e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3152e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3092e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3033e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2973e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2850e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2793e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2734e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2626e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2576e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2525e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.2472e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.2419e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.2367e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2313e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2205e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2153e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.2100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.2050e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.2001e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.1954e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.1906e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.1856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1757e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1708e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1658e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.1609e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.1561e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.1512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.1465e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.1420e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.1375e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.1330e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.1283e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.1237e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.1191e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.1144e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.1097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.1050e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.1004e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.0958e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.0912e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0828e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0785e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.0699e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.0655e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.0612e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.0568e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.0524e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.0481e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.0438e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.5355e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9772 - val_loss: 0.2122 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4983 Epoch 32/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.8387e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.9462e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.8726e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.6780e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.5232e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.4065e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.3162e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.2340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.1512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 7.0929e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 7.0554e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.0243e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.0051e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.9950e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.9845e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.9719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.9571e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.9422e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.9287e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.9142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.8973e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.8831e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.8734e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.8636e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.8566e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.8519e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.8476e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.8422e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.8358e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8298e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8243e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8185e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8049e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8002e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.7952e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7908e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7877e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7815e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7776e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7737e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7698e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7657e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7611e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7566e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7528e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7487e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7450e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7419e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7391e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7325e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7288e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7251e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7215e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.7175e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.7137e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.7102e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7066e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7032e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7002e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.6973e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.6942e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.6909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.6875e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.6841e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.6807e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.6771e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6736e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6702e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6668e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6636e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6608e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6580e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6551e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6522e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6461e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6431e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6399e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6367e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6337e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6306e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6277e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6251e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.6225e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.6198e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.6170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6085e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.6025e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5997e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5969e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5943e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5894e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5842e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5815e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5789e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5762e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5734e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5706e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5679e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5625e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5599e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5574e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5547e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5520e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.5493e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.5466e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.5438e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5410e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5354e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5328e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.2150e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9773 - val_loss: 0.2172 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4984 Epoch 33/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.0773e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.1940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.1858e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.0978e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.0280e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.9671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.9173e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.8735e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.8289e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7947e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7762e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7456e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.7408e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.7375e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.7297e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.7198e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.7114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.7043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.6963e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.6867e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.6789e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.6736e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.6677e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.6630e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.6598e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.6572e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.6534e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.6489e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.6442e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.6396e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.6346e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.6291e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.6242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.6203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.6164e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.6131e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.6108e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.6088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.6064e-05 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.5822e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.5795e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.5771e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.5749e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.5725e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5698e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5644e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5616e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.5586e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.5557e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.5531e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5504e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5479e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5456e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5433e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.5408e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.5382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.5355e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.5329e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.5302e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.5274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.5246e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.5220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.5193e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.5168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.5145e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.5122e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.5100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5077e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5054e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4987e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4965e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.4925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.4905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.4887e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.4869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.4851e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.4833e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4815e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4796e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4777e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4757e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4737e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4699e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4647e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4630e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4612e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4593e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4557e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4538e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4520e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4485e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4468e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4452e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4437e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4421e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4404e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4387e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4370e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4353e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4336e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4319e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4302e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.2275e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9772 - val_loss: 0.2215 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4984 Epoch 34/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.0525e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.9778e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.8632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.7118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.5752e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.4683e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.4061e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3580e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3110e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2740e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2468e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2186e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.1995e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1888e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1793e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1688e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.1568e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.1445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1335e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1228e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.1112e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.1000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.0926e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.0842e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.0771e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.0718e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.0667e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.0611e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.0550e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0487e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0429e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0375e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0315e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0213e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0164e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0083e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.0021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9987e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9952e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9842e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9741e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9682e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9656e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9627e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9595e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9565e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9534e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9505e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9474e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9444e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9418e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9390e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9365e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9343e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9321e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9298e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9249e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.9225e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.9202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.9177e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9152e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9130e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9107e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9085e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9064e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.9044e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.9023e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9002e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.8980e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.8958e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8936e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8912e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8889e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8867e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8845e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8824e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8804e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8785e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8764e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8743e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8723e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8702e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8660e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8639e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8619e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8598e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8578e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8559e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8540e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8521e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8501e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8481e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8461e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8442e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8421e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8401e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8363e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8344e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8325e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8307e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8289e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8270e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8251e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8233e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8214e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8195e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8176e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8158e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8139e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.5918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2247 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 35/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 5.0164e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.0678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.0124e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.9144e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.8203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.7421e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.6842e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.6344e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.5897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.5557e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.5359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5159e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5015e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.4932e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.4860e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4768e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4669e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4573e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4483e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.4390e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.4299e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.4211e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.4142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.4064e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.4000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.3954e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.3918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.3873e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.3824e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.3777e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.3730e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.3685e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.3634e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3588e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3553e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3516e-05 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2433e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2418e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2404e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2389e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2375e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2335e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2321e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2307e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2278e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2263e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2249e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2235e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2206e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2193e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2180e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2167e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2087e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2074e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2060e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.0468e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2279 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 36/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.2945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.3934e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.3710e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.2833e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.1967e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.1273e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.0784e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.0386e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.9982e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.9686e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.9507e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.9314e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.9174e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.9077e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.8994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.8896e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.8784e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.8677e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8483e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8383e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8294e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8223e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8099e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8056e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8018e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7972e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7926e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.7882e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.7841e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.7800e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.7755e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.7712e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.7678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.7642e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.7608e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.7580e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7554e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7525e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7471e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7417e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7389e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7363e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7316e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7295e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7277e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7240e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7200e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7181e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.7161e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.7140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.7121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.7104e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.7086e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.7071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.7058e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.7046e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.7032e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.7018e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.7002e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6988e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6973e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.6957e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6942e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6929e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6915e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6903e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6893e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6883e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6860e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6848e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6836e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6825e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6813e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6801e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.6790e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.6780e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.6769e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.6759e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6750e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6740e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6729e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6698e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6688e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6677e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6668e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6659e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6651e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6636e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6629e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6621e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6613e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6605e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6596e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6587e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6578e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6569e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6560e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6551e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6535e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6527e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6518e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.6510e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.6501e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.6492e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.6483e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.6474e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.6465e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.6456e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.5389e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2310 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 37/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.7123e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.8567e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.8424e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.7741e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.7029e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.6480e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.6124e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.5816e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5505e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5269e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4951e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4830e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4746e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4683e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4602e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4513e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4421e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.4340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.4262e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.4181e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.4108e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.4055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.4011e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.3977e-05 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3419e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3415e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3411e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3408e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3405e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3403e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3399e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3395e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3390e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3385e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3379e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3368e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3363e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3357e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3352e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3347e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3343e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3338e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3332e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3326e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3321e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3315e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3309e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3303e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3297e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3291e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3280e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3269e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3263e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3257e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3251e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3245e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3239e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3233e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3227e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3221e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.2493e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2331 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 38/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.4898e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5696e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.5786e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.5282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.4656e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.4121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3737e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3402e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3072e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2813e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2642e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2473e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2345e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2262e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2219e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2078e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.2009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.1946e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.1882e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.1813e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.1753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.1709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.1663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.1632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1611e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1595e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1576e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1554e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1529e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1505e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1479e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1448e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1420e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1397e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1350e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1334e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.1323e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1310e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1294e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1278e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1261e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1245e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1228e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1212e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1198e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1184e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1173e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1164e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1160e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1155e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1151e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1146e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1133e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1125e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1120e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1108e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1115e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1122e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1126e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1129e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1133e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1134e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1133e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1133e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1134e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1136e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1155e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1173e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1189e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1204e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1217e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1229e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1241e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1251e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1261e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1271e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1280e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1289e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1314e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1337e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1379e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1398e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1416e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1433e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1448e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1463e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1478e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1505e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1539e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1572e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1603e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1633e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1661e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1688e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1715e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1739e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1763e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1809e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1831e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1903e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1937e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1970e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2001e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2061e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2089e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2143e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2169e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5236e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2352 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 39/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.1926e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0418e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1212e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0970e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.0510e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.0044e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.6204e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.2392e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.8912e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.5853e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.3202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.0800e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.8651e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.7026e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.5526e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.4100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.2754e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1490e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.0307e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.9193e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.8135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7137e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.6208e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5322e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4488e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.3805e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.3154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.2522e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.1911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.1321e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.0753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.0205e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.9672e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.9159e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.8669e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.8202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.7753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.7390e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.7040e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.6694e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.6354e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.6023e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.5700e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.5385e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.5075e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.4772e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.4479e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.4191e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.3911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.3658e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.3412e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.3168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.2928e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.2692e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.2462e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.2235e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.2012e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.1793e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.1579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.1368e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.1163e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.0972e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0784e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0599e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0416e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0235e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0058e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.9883e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.9710e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.9539e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.9373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.9209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.9047e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8892e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8739e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8588e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.8439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.8291e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.8146e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.8003e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7861e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7721e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7585e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7449e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7316e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.7059e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.6932e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.6806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.6682e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.6560e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.6439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.6320e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.6202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.6086e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.5971e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.5858e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.5747e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.5639e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.5531e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.5424e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.5318e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.5213e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.5110e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.5007e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.4905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4805e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4706e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4609e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.4512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.4418e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.4324e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.4231e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.4139e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.4048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.3958e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.3869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.3780e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.3693e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.3607e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.3323e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2377 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 40/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.7567e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.8599e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.8746e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.8384e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.7970e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.7609e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.7325e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.7081e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6827e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6634e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6519e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6414e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.6344e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.6309e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.6281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.6242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.6203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.6159e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.6122e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6102e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6074e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6049e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6036e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6029e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6029e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6030e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6024e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6014e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6002e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5989e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5976e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5958e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5941e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5929e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5915e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5904e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5898e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5892e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5884e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5861e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5850e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5837e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5823e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5809e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5796e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5783e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5771e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5761e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5731e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5698e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5686e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5675e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5653e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5635e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5627e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5618e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5609e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5600e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5590e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5580e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5569e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5559e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5550e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5540e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5532e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5523e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5515e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5507e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5488e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5479e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5470e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5460e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5450e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5441e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5432e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5423e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5415e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5407e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5398e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5389e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5372e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5363e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5354e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5345e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5336e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5328e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5320e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5313e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5305e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5298e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5290e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5266e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5257e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5249e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5241e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5233e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5225e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5218e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5195e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5188e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5180e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5173e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5165e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5158e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5151e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5143e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2408 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 41/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4644e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.5663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.5800e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.5575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5226e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4680e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4470e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4256e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4093e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4006e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3917e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3855e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3829e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3739e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3697e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3665e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3629e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3593e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3562e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3542e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3517e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3501e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3492e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3486e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3476e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3462e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3447e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3432e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3415e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3395e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3377e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3364e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3348e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3334e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3324e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3314e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3304e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.3283e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3272e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3261e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3249e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3237e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3226e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3214e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3195e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3178e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3169e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3159e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3149e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3130e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3112e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3103e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3095e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3081e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3073e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3064e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3046e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3037e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3027e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3018e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3010e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3001e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2993e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2986e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2979e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2972e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.2964e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.2956e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.2948e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.2939e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.2931e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.2923e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2915e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2908e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2893e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2886e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2879e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2864e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2840e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2832e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2825e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2817e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.2810e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.2803e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.2797e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2790e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2782e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2775e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2768e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2761e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2746e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2739e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2732e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2726e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2713e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2707e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2701e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2694e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2688e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2682e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2676e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2669e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2657e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.1931e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2439 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 42/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.4672e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.4849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.4604e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.4079e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6957e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8388e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.9130e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.9475e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.9585e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.9578e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.9530e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9420e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9311e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9106e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.8984e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.9832e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.0511e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.1065e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.1510e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.1881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2189e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2467e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2692e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2894e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.3087e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.3261e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.3410e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.7046e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.8352e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.9828e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.1254e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.2684e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.4229e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.5847e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.7889e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9986e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.2111e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.4510e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.7132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.9814e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.2590e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.5556e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.8588e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.1763e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.5143e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.8727e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0270e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0702e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1150e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1648e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2177e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2721e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3271e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3843e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4438e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5063e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5701e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6357e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7058e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7795e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8565e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9390e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0273e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9999 - loss: 2.1180e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9999 - loss: 2.2102e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9999 - loss: 2.3049e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 2.4025e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 2.5017e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 2.6039e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 2.7094e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9999 - loss: 2.8183e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9999 - loss: 2.9325e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9999 - loss: 3.0504e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9999 - loss: 3.1730e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9999 - loss: 3.3019e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9999 - loss: 3.4341e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 3.5680e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 3.7054e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 3.8460e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 3.9896e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9999 - loss: 4.1361e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 4.2849e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 4.4389e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 4.5985e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 4.7621e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9998 - loss: 4.9321e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 5.1069e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 5.2850e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 5.4663e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 5.6505e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9998 - loss: 5.8376e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 6.0284e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 6.2213e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 6.4160e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 6.6149e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 6.8175e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 7.0227e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 - val_accuracy: 0.9708 - val_loss: 0.1972 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4953 Epoch 43/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9945 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9942 - loss: 0.0144 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9942 - loss: 0.0145 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9943 - loss: 0.0142 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9944 - loss: 0.0141 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9945 - loss: 0.0140 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9945 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9946 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9946 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9947 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9947 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9947 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9947 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9947 - loss: 0.0139 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9947 - loss: 0.0138 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9947 - loss: 0.0137 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9947 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9948 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9948 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9948 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9948 - loss: 0.0136 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9948 - loss: 0.0135 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9948 - loss: 0.0135 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9948 - loss: 0.0135 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9948 - loss: 0.0135 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9948 - loss: 0.0134 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9948 - loss: 0.0134 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9948 - loss: 0.0134 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9949 - loss: 0.0134 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9949 - loss: 0.0133 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9949 - loss: 0.0133 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9949 - loss: 0.0133 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9949 - loss: 0.0133 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9949 - loss: 0.0132 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9949 - loss: 0.0132 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9949 - loss: 0.0132 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9949 - loss: 0.0131 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9950 - loss: 0.0131 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9950 - loss: 0.0131 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9950 - loss: 0.0131 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9950 - loss: 0.0130 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9950 - loss: 0.0130 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9950 - loss: 0.0130 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9950 - loss: 0.0129 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9950 - loss: 0.0129 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9950 - loss: 0.0129 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9951 - loss: 0.0129 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9951 - loss: 0.0128 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9951 - loss: 0.0128 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9951 - loss: 0.0128 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9951 - loss: 0.0127 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9951 - loss: 0.0127 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9951 - loss: 0.0127 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9951 - loss: 0.0127 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9951 - loss: 0.0126 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9952 - loss: 0.0126 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9952 - loss: 0.0126 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9952 - loss: 0.0126 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9952 - loss: 0.0125 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9952 - loss: 0.0125 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9952 - loss: 0.0125 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9952 - loss: 0.0124 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9952 - loss: 0.0124 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9952 - loss: 0.0124 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9952 - loss: 0.0124 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9953 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9953 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9953 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9953 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9953 - loss: 0.0123 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9953 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9953 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9953 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9953 - loss: 0.0122 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9953 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9953 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9953 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9954 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9954 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9954 - loss: 0.0121 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9954 - loss: 0.0120 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 - val_accuracy: 0.9721 - val_loss: 0.1743 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4955 Epoch 44/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9960 - loss: 0.0102 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9956 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9955 - loss: 0.0112 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4964  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9956 - loss: 0.0111 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9956 - loss: 0.0111 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9956 - loss: 0.0111 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9956 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4966  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9957 - loss: 0.0110 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4968  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9957 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9958 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9958 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9958 - loss: 0.0109 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9958 - loss: 0.0108 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9958 - loss: 0.0107 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9958 - loss: 0.0107 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9958 - loss: 0.0107 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9958 - loss: 0.0107 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9958 - loss: 0.0107 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9959 - loss: 0.0107 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9959 - loss: 0.0106 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9959 - loss: 0.0106 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9959 - loss: 0.0106 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9959 - loss: 0.0106 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9959 - loss: 0.0106 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9959 - loss: 0.0106 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9959 - loss: 0.0105 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9959 - loss: 0.0105 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9959 - loss: 0.0105 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9959 - loss: 0.0105 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9959 - loss: 0.0105 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9960 - loss: 0.0104 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9960 - loss: 0.0104 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9960 - loss: 0.0104 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9960 - loss: 0.0104 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9960 - loss: 0.0103 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9960 - loss: 0.0103 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9960 - loss: 0.0103 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9960 - loss: 0.0103 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9960 - loss: 0.0103 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9960 - loss: 0.0102 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9960 - loss: 0.0102 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9961 - loss: 0.0102 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9961 - loss: 0.0102 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9961 - loss: 0.0101 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9961 - loss: 0.0101 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9961 - loss: 0.0101 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9961 - loss: 0.0101 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9961 - loss: 0.0100 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9961 - loss: 0.0100 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9961 - loss: 0.0100 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9961 - loss: 0.0100 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9962 - loss: 0.0099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9962 - loss: 0.0099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9962 - loss: 0.0099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9962 - loss: 0.0099 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9962 - loss: 0.0098 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9962 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4971  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9962 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9963 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9963 - loss: 0.0097 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9963 - loss: 0.0096 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9963 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9964 - loss: 0.0095 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9964 - loss: 0.0094 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4972 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9964 - loss: 0.0092 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9973 - loss: 0.0071 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978 - val_accuracy: 0.9745 - val_loss: 0.1744 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4965 Epoch 45/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9979 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9979 - loss: 0.0058 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9980 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9981 - loss: 0.0052 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9982 - loss: 0.0051 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9982 - loss: 0.0051 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9981 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 - val_accuracy: 0.9746 - val_loss: 0.1766 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4967 Epoch 46/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.9978 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9977 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9976 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9977 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9978 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9978 - loss: 0.0056 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9979 - loss: 0.0054 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9980 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9980 - loss: 0.0052 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9980 - loss: 0.0051 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9980 - loss: 0.0051 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9981 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9981 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9981 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9981 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9982 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9983 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9983 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9983 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9983 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9983 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9984 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9984 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9984 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9986 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9986 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9986 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9986 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 - val_accuracy: 0.9770 - val_loss: 0.1941 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4980 Epoch 47/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 8.5026e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 8.6118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 8.5460e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 8.3423e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 8.1528e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 8.0278e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 7.9368e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.8541e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.7683e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 7.6988e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 7.6486e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.5951e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.5535e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.5239e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.4974e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.4657e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.4314e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.3976e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.3649e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.3343e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.3032e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.2774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.2535e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.2282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.2049e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.1837e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.1627e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.1401e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.1168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.0939e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.0719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.0498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0273e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0054e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.9846e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.9634e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.9435e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.9244e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.9058e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.8870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.8681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.8491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.8308e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.8127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.7944e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.7765e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.7591e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7416e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7245e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7081e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6762e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6442e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6129e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5972e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5817e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5667e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5518e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5372e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5232e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5095e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4957e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4818e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4680e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4406e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4268e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3999e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3740e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3614e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3490e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3366e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.3242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.3119e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2997e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2876e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2755e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2635e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2517e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2400e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2172e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2061e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1950e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1840e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1730e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1622e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1515e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1407e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1303e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1199e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.0995e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.0896e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.0797e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0699e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0406e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.0310e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.0213e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.0117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.0022e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.9927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.9834e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9561e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9471e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.9381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.9292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.9203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.9114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.9025e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.8938e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.8851e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.8468e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2142 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4983 Epoch 48/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.9106e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.9723e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.9831e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.9247e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.8593e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.8068e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.7676e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.7400e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.7097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.6854e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.6694e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6544e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6429e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6378e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6338e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6278e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6161e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6101e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6050e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5996e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5956e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5906e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5867e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5838e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5817e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5750e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5712e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5675e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5636e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.5590e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.5545e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.5506e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.5464e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.5427e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.5395e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.5365e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5333e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.5296e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5258e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5223e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5189e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5152e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5083e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5047e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5017e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4991e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4965e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4938e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4879e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4819e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4756e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4726e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4696e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4667e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4640e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4613e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4585e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4556e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4527e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4469e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4409e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4352e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4324e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4299e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4250e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4224e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4198e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4173e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.4147e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.4121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.4094e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4069e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4018e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3970e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3920e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3894e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3843e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.3817e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.3791e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.3766e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.3741e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.3716e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.3691e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.3667e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.3643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.3618e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.3594e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.3570e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.3546e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.3521e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.3497e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.3473e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.3449e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.3425e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.3402e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.3379e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.3356e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.3332e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.3308e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.3285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.3261e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.3237e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.3213e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.3190e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.3166e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.0362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2260 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4984 Epoch 49/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.0223e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0923e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0947e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0469e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.9907e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.9478e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.9171e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.8872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.8565e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.8321e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.8152e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7980e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7882e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7820e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.7768e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7700e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7625e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7558e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7422e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7356e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7241e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7105e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6985e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6895e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6807e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6765e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6728e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6691e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6660e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6634e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6610e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6596e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6561e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6525e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6507e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6487e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6470e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6453e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6438e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6424e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6411e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6396e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6379e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6346e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6329e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6310e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6275e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6258e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6227e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6213e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6199e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6184e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6152e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6137e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6120e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6104e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6078e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6069e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6060e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6051e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6041e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6030e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6020e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5998e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5987e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5975e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5964e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5953e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5942e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5932e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5912e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5901e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5890e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5880e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5858e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5847e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5836e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5824e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5814e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5805e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5795e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5785e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5775e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5764e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5754e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5743e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5733e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5722e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5711e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5701e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5691e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5662e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5642e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5622e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5612e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5602e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5592e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.5582e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.4409e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2328 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 50/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4450e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.4827e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4124e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3602e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2755e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2548e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2384e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2188e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2126e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2068e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2038e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1998e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1960e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1886e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1843e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1807e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1779e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1754e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1733e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1707e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1692e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1672e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1633e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1615e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1594e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1572e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1556e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1537e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.1521e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.1510e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.1501e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1489e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1475e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1460e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1431e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1415e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1400e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1387e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1372e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1348e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1336e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1324e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1311e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1297e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1283e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1269e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1254e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1239e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1226e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1212e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1198e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1186e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1175e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1163e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1150e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1138e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1126e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1102e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1078e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1066e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1054e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1033e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1022e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.1011e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0999e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0988e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0964e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0952e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0928e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0917e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0906e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0895e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0885e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0874e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0864e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0853e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0842e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0831e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0820e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0810e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0799e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0789e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0779e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0770e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0760e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0750e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0739e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0729e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0700e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0692e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0684e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0676e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0668e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0660e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0644e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0636e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0627e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0619e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0611e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0603e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0595e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0587e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.9622e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2379 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 51/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.1458e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.1704e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.1507e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.1026e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.0498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.0097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9852e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9738e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9615e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9437e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9350e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9280e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9229e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9183e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9134e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9077e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9020e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8972e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8928e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8878e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8828e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8743e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8705e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8676e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8650e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8625e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8598e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8571e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8545e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8527e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8506e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.8485e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.8466e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8426e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8412e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8397e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8380e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8363e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8345e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8328e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8311e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.8257e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.8240e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.8223e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8208e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8194e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8181e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8167e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8112e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8083e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8069e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8019e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8006e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7982e-05 - 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0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7687e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7670e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7662e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7653e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7645e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7636e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7628e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7620e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7611e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7602e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7594e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7585e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7577e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7569e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7561e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7553e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7545e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7536e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7528e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7520e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7504e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7496e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7488e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6540e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2426 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 52/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7030e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6934e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6607e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6302e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6493e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6659e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6775e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6809e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6816e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6829e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6815e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6822e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6834e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6845e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6841e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6825e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6804e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6781e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6760e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6732e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6705e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6686e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6679e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6680e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6677e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6668e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6658e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6648e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6638e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6624e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6609e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6596e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6581e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6566e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6554e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6542e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6530e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6516e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6501e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6487e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6471e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6455e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6438e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6423e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6408e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6395e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6370e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6358e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6345e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6332e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6320e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6307e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6295e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6271e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6248e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6238e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6227e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6217e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6206e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6196e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6186e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6176e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6166e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6155e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6145e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6125e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6108e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6099e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6089e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6080e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6061e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6052e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6034e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6026e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6017e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6001e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5992e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5984e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5975e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5967e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5959e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5951e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5942e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5935e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5919e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5904e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5889e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5882e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5875e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5867e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5859e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5852e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5844e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5836e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5829e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5822e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5815e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5808e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5801e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5795e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5788e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5781e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5775e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5768e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5762e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5755e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4983e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2453 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 53/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5641e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5215e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4610e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4453e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4317e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4166e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4072e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3953e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3915e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3896e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3891e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3878e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3835e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3819e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3802e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3782e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3763e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3749e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3733e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3722e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3716e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3712e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - 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1.3666e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3662e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3659e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3655e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3648e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3642e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3637e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3627e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3623e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3620e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3617e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3613e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3610e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3607e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3603e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3598e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3596e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3593e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3590e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3587e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3585e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3583e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3581e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3578e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3571e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3568e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3564e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3560e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3556e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3553e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3549e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3546e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3539e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3535e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3530e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3526e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3521e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3517e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3508e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3499e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3495e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3490e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3486e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3481e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3477e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3472e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3467e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3462e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3457e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3452e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3448e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3443e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3438e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3434e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3430e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3425e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3420e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3416e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3411e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3407e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3403e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3400e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3396e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3392e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3388e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3385e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3377e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3369e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3366e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3358e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3354e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3350e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.2885e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2484 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 54/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 1.3526e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3714e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3454e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3222e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3051e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2895e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2743e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2617e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2523e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2438e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2376e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2348e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2328e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2307e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2287e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2264e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2245e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2225e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2180e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2163e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2145e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2123e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2108e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2098e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2079e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2062e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2053e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2047e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2040e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2036e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2035e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2033e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2027e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2022e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2018e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2014e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2008e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2003e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1999e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1989e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1985e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1982e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1979e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1975e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1971e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1967e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1963e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1959e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1954e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1951e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1948e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1943e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1938e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1935e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1932e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1930e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1929e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1928e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1926e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1924e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1924e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1924e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1923e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1921e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1920e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1919e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1917e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1915e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1914e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1913e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1910e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1908e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1906e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1903e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1901e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1899e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1896e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1894e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1893e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1891e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1889e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1888e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1886e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1885e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1883e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1880e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1878e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1877e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1875e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1874e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1867e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1865e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1863e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1862e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1860e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1672e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2514 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 55/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2465e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.2466e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.2445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.2207e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.2007e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.1867e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.1745e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1647e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1578e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1478e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1409e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.1394e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.1382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1337e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1318e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.1300e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.1281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.1259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1238e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1190e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.1183e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.1177e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.1169e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.1161e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.1153e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.1146e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.1141e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.1135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.1130e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.1126e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.1122e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.1118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.1117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.1117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.1116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.1114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.1111e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.1109e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.1107e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.1104e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.1101e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.1098e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.1095e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.1092e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.1090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.1088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.1084e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.1080e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.1076e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.1072e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1068e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1063e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1059e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.1051e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.1046e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.1043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.1040e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.1037e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.1033e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1029e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1025e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1017e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1013e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1004e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0997e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0990e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.0987e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.0983e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.0979e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0975e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0971e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0967e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0964e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0960e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0956e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0953e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0950e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0947e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0943e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0936e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0933e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0929e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0921e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0914e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0907e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.0904e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.0900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.0897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.0893e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.0890e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.0886e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.0883e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.0879e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.0876e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.0872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0866e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0863e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0859e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0852e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0845e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0841e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0838e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0834e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.0399e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2540 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 56/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.0724e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.0724e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.0697e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0547e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0412e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0303e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0150e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0066e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.9968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.9499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.9017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.8774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.8640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.8535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.8427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.8306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.8153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.8092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.8039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.7936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.7856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.7793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.7739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.7695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.7686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.7677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.7649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.7604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.7232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.7222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.7220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.7115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.7081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.7047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.7019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.6987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.6964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.6863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.6844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.6825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.6801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.6779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.6759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.6735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.6713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.6695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.6678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.6664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.6647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.6631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.6614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.6597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.6574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.6273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.6252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.6233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.6216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.6195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.6175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.6153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.6131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.6108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.6084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.6059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.6037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.6014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.5992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.5971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.5951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.5931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.5909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.5889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.5870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.5851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.5832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.5812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.5792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.5772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.5753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.5734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.5717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.5699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.5679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.5661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.5642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.5629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.5617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.5605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.5593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.5581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.4142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2561 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 57/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.3216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.5542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.6243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.5047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.3870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.2889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.2340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.1868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.1253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0685e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.0117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.0075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.0035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.0002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.9009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.6732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2585 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 58/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.1435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.3308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.3082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.1121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 8.9145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.7561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.6439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.5590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.4794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.4292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.3948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.3624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.3406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.3283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.3178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.3050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2479e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.1989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.1941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.1927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.1930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.1913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.1880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1815e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.1709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.1682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.1647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.1612e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.1592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.1583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.1561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.1532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.1503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.1478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.1451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.1420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.1387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.1359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.1328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.1305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.1291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.1282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.1269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.1257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.1244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.1227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.1211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.1191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.1173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.1156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.1073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.1055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.1020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.1005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.0988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.0614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.0598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.0579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.0561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.0544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.0527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.0443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.0425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.0406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.0388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.0369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.0351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.0286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.0269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.0252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.0235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.8147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2607 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 59/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.3840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.6045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.4890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.3276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.2744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.1979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.1400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.1003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.0449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.9944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.9625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.9256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.8956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.8755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.8589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.8410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.8226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.8039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.7849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.7687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.7498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.7318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.7176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.7032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.6915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.6845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.6783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 7.6710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.6632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.6553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.6478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.6393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.6302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.6213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.6134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.6067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.6011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.5970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.5935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.5895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.5872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.5846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.5821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.5795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.5760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.5725e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.5691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.5655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.5623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.5595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.5569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.5544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.5516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.5488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.5460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.5335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.5319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.5307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.5220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.5200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.5179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.5156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.5136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.5114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.4996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.4976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.4955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.4935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.4916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.4897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.4879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.4862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.4845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.4826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.4806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.4785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.4764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.4743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.4722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.4701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.4682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.4662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.4642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.4623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.4604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.4585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.4564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.4543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.4522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.4306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.1849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2626 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 60/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.9752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.8086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.6077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.1083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.9471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.8220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.7210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.6381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.5704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.5164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.4633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.4212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.3339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.3039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.2741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.2479e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.2229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.1980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.1751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.1569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.1387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.1238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.1139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.1047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - 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7.0310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.0177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.0126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.0084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.0031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.9870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.9818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.9766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.9714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9611e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.8919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.8905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.8892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.8878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.8863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.8792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.8785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.8778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.8763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.8757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.8752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.8748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.8742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.8738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.8732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.8726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.8721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.8716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.8711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.8684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.7813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2644 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 61/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.9514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.0183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.0263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.9111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.8182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.7454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.7073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.5241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.5151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3815e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3782e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.3709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.3693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.3676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.3652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.3629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.3610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.3588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.3569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.3556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.3544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.3531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.3458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.3444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.3432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.3364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.3358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.3351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.3345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.3339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.3335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.3329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.3322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.3313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.3306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.3298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.3289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.3279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.3270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.3260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.3251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.3243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.3236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.3226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.3217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.3207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.3196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.3185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.3172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.3160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.3148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.3136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.3124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.3113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.3102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.3090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.3077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.3064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.3051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.3037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.3022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.3006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.2991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.2975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.2961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.2948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.2935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.2922e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.2910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.2898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.2886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.2874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.2861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.2847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.2836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.2824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 6.1463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2668 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 62/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.3279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.1637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.9399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.7616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.5969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.4843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.3989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.3379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.2937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.2601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.2482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.2323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.2204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.2180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.2157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.2103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.2025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.1937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.1856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.1775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.1675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.1597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0782e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.0235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.0225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.0214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.0202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.0191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.0179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.0126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.0118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.0111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.0102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.0093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.0082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.9784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.9770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.9757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.9654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.9640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.9626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.9612e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.9597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.9583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.9569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.7857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2682 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 63/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.0396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.9954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.9252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.8093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.7067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.6249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.5672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.5224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.4821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.4533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.4411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.4299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.4307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.4326e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.4346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.4439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.4444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.4449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.4455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.4470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.4478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.4485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.4493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.4499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.4504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.4511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.4518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.4522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.4523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.4525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.4532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.4531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.4526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.4526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.4526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.4525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.4523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.4520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.4516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.4511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.4505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.4501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.4494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.4489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.4485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.4481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.4475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.4468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.4461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.4454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.4446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.4437e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.4427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.4419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.4424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.4430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.4436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.4443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.4448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.4451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.4454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.4458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.4460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.4461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.4462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.4465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.4466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.4491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.4493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.4494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.4591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2698 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 64/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 6.2109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.1087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.0608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.9303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.8203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.7654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.7122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.6724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.6242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.5614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.5148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.5032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.4224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.4053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3922e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.3863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.3838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.3816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.3788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.3755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.3719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.3560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.3539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.3522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.3505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.3487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.3470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.3348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.3323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.3300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.3278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.3025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.3010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.2829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.0522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2714 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 65/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 5.1469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.2002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.1724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.1280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.0638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.0114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.9385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.8738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.8547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.8344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.7907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.7877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.7833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.7796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.7773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.7764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.7772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.7781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.7790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.7788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.7782e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.7730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.7701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.7688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.7676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.7674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.7674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.7676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.7673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.7669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.7639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.7642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.7640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.7636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7530e-06 - 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0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.7424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.7422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.7420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.7410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.7406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.7403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.7400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.7397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.7394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.7559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.7589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.7619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.0812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2735 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 66/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.1700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0042e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1107e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0834e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0288e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.7109e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.1706e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.6801e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2386e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8437e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.4900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8826e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.6397e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4172e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.2115e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.0210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8440e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6794e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5258e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3818e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2471e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.1210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.0022e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.8903e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.7900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.6971e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.6086e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.4435e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2218e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.1543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.0898e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.0278e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.9685e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9231e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.8810e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.8398e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.7996e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.7604e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.7222e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6850e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6492e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6145e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.5839e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.5540e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.5253e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.5456e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.5677e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.5881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6342e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.7222e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.7580e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.8044e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.8536e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.9124e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0240e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.1369e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.2538e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.3907e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.5440e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.7042e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.8723e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.0722e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.2945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.5517e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.8265e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1506e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.5340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.9503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.4076e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.9209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.4834e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.0760e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.7099e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0387e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1141e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1968e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2850e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3822e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4874e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.5985e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7135e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8348e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 1.9606e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.0917e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.2282e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.3690e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 2.5194e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 2.6772e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 2.8434e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 3.0216e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 3.2120e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 3.4103e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 3.6142e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 3.8290e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 4.0541e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 4.2897e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 4.5318e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9998 - loss: 4.7846e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9998 - loss: 5.0505e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9998 - loss: 5.3316e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9998 - loss: 5.6210e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9998 - loss: 5.9246e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9998 - loss: 6.2433e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9998 - loss: 6.5749e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9998 - loss: 6.9133e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9998 - loss: 7.2678e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9997 - loss: 7.6345e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9997 - loss: 8.0086e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9997 - loss: 8.3898e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9997 - loss: 8.7768e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9997 - loss: 9.1723e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9997 - loss: 9.5773e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9997 - loss: 9.9888e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9979 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 - val_accuracy: 0.9537 - val_loss: 2.4131 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4973 Epoch 67/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9883 - loss: 0.0307 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9882 - loss: 0.0313 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9883 - loss: 0.0311 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9886 - loss: 0.0305 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9888 - loss: 0.0298 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4928  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9890 - loss: 0.0294 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9891 - loss: 0.0289 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4929  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9893 - loss: 0.0285 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9894 - loss: 0.0281 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4930  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9895 - loss: 0.0278 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9896 - loss: 0.0276 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9897 - loss: 0.0273 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9897 - loss: 0.0271 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9898 - loss: 0.0270 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9899 - loss: 0.0268 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9899 - loss: 0.0266 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4931  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9900 - loss: 0.0264 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9900 - loss: 0.0262 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9901 - loss: 0.0260 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9902 - loss: 0.0258 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4932  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9902 - loss: 0.0257 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9903 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9903 - loss: 0.0253 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9904 - loss: 0.0252 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9904 - loss: 0.0250 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9905 - loss: 0.0249 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9905 - loss: 0.0248 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9906 - loss: 0.0246 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9906 - loss: 0.0245 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9907 - loss: 0.0244 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9907 - loss: 0.0242 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9908 - loss: 0.0241 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9908 - loss: 0.0240 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9908 - loss: 0.0238 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9909 - loss: 0.0237 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9909 - loss: 0.0236 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9910 - loss: 0.0235 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9910 - loss: 0.0234 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9911 - loss: 0.0233 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9911 - loss: 0.0231 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9911 - loss: 0.0230 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9912 - loss: 0.0229 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9912 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9913 - loss: 0.0227 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9913 - loss: 0.0226 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9913 - loss: 0.0225 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9914 - loss: 0.0224 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4939  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9914 - loss: 0.0222 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4939  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9915 - loss: 0.0221 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4939  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9915 - loss: 0.0220 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4939  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9915 - loss: 0.0219 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4939  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9916 - loss: 0.0218 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9916 - loss: 0.0217 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9917 - loss: 0.0216 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9917 - loss: 0.0215 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9918 - loss: 0.0214 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9918 - loss: 0.0213 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9918 - loss: 0.0212 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9919 - loss: 0.0211 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9919 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9920 - loss: 0.0208 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9920 - loss: 0.0207 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9920 - loss: 0.0206 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9921 - loss: 0.0205 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9921 - loss: 0.0204 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9921 - loss: 0.0203 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9922 - loss: 0.0202 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9922 - loss: 0.0201 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9923 - loss: 0.0200 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9923 - loss: 0.0199 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9923 - loss: 0.0198 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9924 - loss: 0.0197 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9924 - loss: 0.0197 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9924 - loss: 0.0196 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9925 - loss: 0.0195 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9925 - loss: 0.0194 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9925 - loss: 0.0193 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9926 - loss: 0.0192 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9926 - loss: 0.0191 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9927 - loss: 0.0190 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9927 - loss: 0.0189 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9927 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9927 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9928 - loss: 0.0187 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9928 - loss: 0.0186 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9928 - loss: 0.0185 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9929 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9929 - loss: 0.0184 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9929 - loss: 0.0183 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9930 - loss: 0.0182 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9930 - loss: 0.0181 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9930 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9931 - loss: 0.0180 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9931 - loss: 0.0179 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9931 - loss: 0.0178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9931 - loss: 0.0178 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9932 - loss: 0.0177 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9932 - loss: 0.0176 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9932 - loss: 0.0175 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9932 - loss: 0.0175 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9933 - loss: 0.0174 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9933 - loss: 0.0173 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9933 - loss: 0.0173 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9933 - loss: 0.0172 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9934 - loss: 0.0172 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9934 - loss: 0.0171 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9934 - loss: 0.0170 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9934 - loss: 0.0170 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9935 - loss: 0.0169 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9935 - loss: 0.0169 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9935 - loss: 0.0168 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9935 - loss: 0.0167 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9936 - loss: 0.0167 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9936 - loss: 0.0166 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9936 - loss: 0.0166 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9936 - loss: 0.0165 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4953 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9936 - loss: 0.0165 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4953 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9937 - loss: 0.0164 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4953 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9937 - loss: 0.0164 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4953 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9937 - loss: 0.0163 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4953 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9960 - loss: 0.0103 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969 - val_accuracy: 0.9736 - val_loss: 0.1748 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4962 Epoch 68/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9966 - loss: 0.0090 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4975  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9965 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4974  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9965 - loss: 0.0093 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4974  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9966 - loss: 0.0090 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4975  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9966 - loss: 0.0089 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4975  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9967 - loss: 0.0087 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9967 - loss: 0.0086 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9968 - loss: 0.0085 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9968 - loss: 0.0084 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9968 - loss: 0.0084 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9968 - loss: 0.0083 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9969 - loss: 0.0083 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9969 - loss: 0.0082 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9969 - loss: 0.0082 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9969 - loss: 0.0082 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9969 - loss: 0.0081 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9969 - loss: 0.0081 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9969 - loss: 0.0081 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9970 - loss: 0.0080 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4976  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9970 - loss: 0.0080 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9970 - loss: 0.0080 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9970 - loss: 0.0079 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9970 - loss: 0.0079 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9970 - loss: 0.0079 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9970 - loss: 0.0079 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9970 - loss: 0.0078 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9970 - loss: 0.0078 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9971 - loss: 0.0078 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9971 - loss: 0.0078 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9971 - loss: 0.0077 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9971 - loss: 0.0077 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9971 - loss: 0.0077 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9971 - loss: 0.0077 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9971 - loss: 0.0076 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9971 - loss: 0.0076 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9971 - loss: 0.0076 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9971 - loss: 0.0076 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9971 - loss: 0.0076 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9972 - loss: 0.0075 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9972 - loss: 0.0075 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9972 - loss: 0.0075 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9972 - loss: 0.0075 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9972 - loss: 0.0074 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9972 - loss: 0.0074 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9972 - loss: 0.0074 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9972 - loss: 0.0074 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9972 - loss: 0.0073 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9972 - loss: 0.0073 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9972 - loss: 0.0073 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9972 - loss: 0.0073 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9973 - loss: 0.0073 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9973 - loss: 0.0072 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9973 - loss: 0.0072 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9973 - loss: 0.0072 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9973 - loss: 0.0072 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4978  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9973 - loss: 0.0071 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9973 - loss: 0.0071 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9973 - loss: 0.0071 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9973 - loss: 0.0071 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9973 - loss: 0.0071 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9973 - loss: 0.0070 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9974 - loss: 0.0070 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9974 - loss: 0.0070 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9974 - loss: 0.0070 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9974 - loss: 0.0069 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9974 - loss: 0.0069 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9974 - loss: 0.0069 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9974 - loss: 0.0069 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9974 - loss: 0.0069 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9974 - loss: 0.0068 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9974 - loss: 0.0068 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9974 - loss: 0.0068 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9974 - loss: 0.0068 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9974 - loss: 0.0068 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9975 - loss: 0.0067 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9975 - loss: 0.0067 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9975 - loss: 0.0067 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9975 - loss: 0.0067 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9975 - loss: 0.0067 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9975 - loss: 0.0066 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9975 - loss: 0.0066 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9975 - loss: 0.0066 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9975 - loss: 0.0066 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9975 - loss: 0.0066 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9975 - loss: 0.0066 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9975 - loss: 0.0065 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9975 - loss: 0.0065 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9976 - loss: 0.0065 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9976 - loss: 0.0065 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9976 - loss: 0.0065 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9976 - loss: 0.0064 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9976 - loss: 0.0064 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9976 - loss: 0.0064 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4980  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9976 - loss: 0.0064 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9976 - loss: 0.0064 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9976 - loss: 0.0064 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9976 - loss: 0.0063 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9976 - loss: 0.0063 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9976 - loss: 0.0063 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9976 - loss: 0.0063 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9976 - loss: 0.0063 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9976 - loss: 0.0062 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9977 - loss: 0.0062 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9977 - loss: 0.0062 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9977 - loss: 0.0062 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9977 - loss: 0.0062 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9977 - loss: 0.0062 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9977 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9977 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9977 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9977 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9977 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9977 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9977 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9977 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9977 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9977 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9978 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9978 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9978 - loss: 0.0060 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 - val_accuracy: 0.9760 - val_loss: 0.1906 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4975 Epoch 69/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9997 - loss: 9.2172e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 - val_accuracy: 0.9767 - val_loss: 0.2100 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4981 Epoch 70/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.3080e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.2122e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0781e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.9127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.7510e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.6152e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.4944e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.4250e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.3481e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.2830e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.2310e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.1745e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.1240e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.0789e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.0353e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.9891e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.9430e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.8967e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.8516e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.8089e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.7661e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.7266e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.6895e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.6525e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.6181e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.5858e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.5543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.5230e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.4921e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4616e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4317e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3724e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3435e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3157e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.2880e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2619e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2368e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2126e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.1885e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.1646e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.1413e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.1184e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.0959e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.0735e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.0515e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.0303e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.0091e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9887e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9688e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9495e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9303e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9112e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.8740e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.8559e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.8382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.8207e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.8037e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7704e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7542e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7224e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7066e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.6910e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.6756e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.6606e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.6457e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.6309e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.6165e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.6021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.5881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.5742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.5606e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.5472e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.5339e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.5207e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.5076e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.4951e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.4826e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.4702e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.4581e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.4460e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.4341e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.4224e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.4108e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.3993e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.3879e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.3766e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.3654e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.3543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.3432e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.3323e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.3215e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.3109e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.3004e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.2900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.2797e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.2695e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.2593e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.2492e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.2393e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.2293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.2195e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.1904e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.1809e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1715e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1622e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1531e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1440e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.1349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.1259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.1170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.1081e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.0992e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.0905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.0818e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.0467e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.2286 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4984 Epoch 71/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3410e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.2975e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2640e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.1564e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1685e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1963e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2072e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.2121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.2154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.2148e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.2142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.2128e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.2081e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.2036e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.1987e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1938e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.1797e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.1753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.1705e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.1663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.1624e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.1584e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.1539e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.1490e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.1439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.1390e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1287e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1245e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1205e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.1164e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.1125e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.1088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.1052e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.1015e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0937e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.0898e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.0860e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.0819e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0780e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0704e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0668e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.0634e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.0601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.0568e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.0534e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.0499e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.0464e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0430e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0395e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0364e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0335e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.0306e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.0277e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.0249e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.0222e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.0193e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.0164e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.0135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.0106e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.0077e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.0048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.0019e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.9991e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.9963e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9935e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9883e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9858e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.9832e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9805e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9779e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9754e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9728e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9703e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.9653e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.9629e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.9606e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.9583e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.9559e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.9536e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.9512e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.9489e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.9465e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9442e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9419e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9396e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.9350e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.9328e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.9306e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9284e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9262e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9240e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9218e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.9196e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.9175e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.9153e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.9132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.9110e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.9089e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9068e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9027e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9007e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.8986e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.8966e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.8946e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8926e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8907e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8887e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.6537e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2404 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 72/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6307e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.5920e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5483e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.5100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4791e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.4549e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.4338e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.4156e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.4012e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.3920e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.3830e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.3776e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.3734e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.3703e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.3663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.3618e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.3575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.3537e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.3500e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.3459e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3421e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3391e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3335e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.3314e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.3297e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.3285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.3269e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.3257e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.3247e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.3236e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.3223e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.3210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.3199e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.3187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.3177e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.3167e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.3158e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.3151e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.3142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.3133e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.3123e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.3113e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.3101e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.3090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.3080e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.3071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3062e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3057e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3053e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3049e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.3043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.3037e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.3031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3023e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3015e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3008e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.2992e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.2985e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.2977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.2971e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.2963e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.2955e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.2947e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.2939e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.2931e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.2922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.2913e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.2905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.2897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.2888e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.2881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.2873e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.2866e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.2858e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.2850e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.2842e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.2833e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.2825e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.2816e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2808e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2800e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2792e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2784e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.2777e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.2769e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.2761e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.2754e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.2747e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.2741e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.2734e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.2728e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.2721e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.2715e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.2709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.2703e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.2697e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2690e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2684e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.2665e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2658e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2645e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2639e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2626e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2620e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2614e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2607e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2600e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2594e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2587e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2581e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2570e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2564e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.1853e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2497 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 73/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1299e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.1248e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1159e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0928e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0756e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0662e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0502e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0425e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0330e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0291e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0267e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0253e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0248e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0250e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0244e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0234e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0225e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0215e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0191e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0182e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0171e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0163e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0156e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0151e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0144e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0126e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0106e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0096e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0086e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0078e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0069e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0060e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0054e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0040e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0032e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0024e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0016e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0008e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.9996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.9908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.9825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.9737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.9654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.9579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.9508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.9430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.9347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.9262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.9179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.9101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.9019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.8936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.8859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.8777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.8698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.8625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.8557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.8489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.8420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.8349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.8282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.8219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.8152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.8090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.8031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.7129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.7073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.7016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.6733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.6680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.6625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.6021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.5965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.5910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.5637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.5582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.8855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2562 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 74/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.3944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.5607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.5837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.4701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.2722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.0854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.0706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.0505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.0459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.0048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.9953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.9858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.9793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.9700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.9626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.9563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.9508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.9500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.9475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.9448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.9434e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.9255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.9249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.9240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.9234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.9226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.9219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.9204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.9006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.8994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.8980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.8962e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.8531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.8510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.8398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 7.8377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 7.8354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.8333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.8311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.8291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.8269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.8245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 7.8222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 7.8198e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 7.8175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 7.8150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 7.8125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 7.8101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 7.8077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 7.8054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 7.8031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 7.8009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 7.7985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 7.7961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 7.7937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 7.7914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 7.7891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 7.7868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 7.7844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 7.7822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 7.7798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 7.4931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2615 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 75/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.4844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 8.1090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.1318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.0256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.8844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 7.7642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.6946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.6322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.5639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.5141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.4733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.4298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.3943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.3653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.3424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.3192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.2961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.2729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 7.2515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 7.2355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 7.2180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.2043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.1921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.1801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.1692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.1623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.1564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.1485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.1393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.1308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.1224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.1166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.1102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.0978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.0909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.0844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.0783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.0728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.0666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.0598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.0527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.0458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.0395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.0333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.0374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.0382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.0383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.0376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.0368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.0358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.0349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.0334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.0318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.0304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.0287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.0273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.0260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.0247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.0230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.0210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.0188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.0167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.0143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.0120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.0097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.0076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.0053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.0032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.0013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.9993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.9971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.9947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.9921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.9896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 6.9873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 6.9848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 6.9823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.9799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.9772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.9745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.9720e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.9694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.9667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.9637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.9606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.9576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.9545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.9513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.9481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.9449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.9417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.9387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.9358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.9334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.9308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.9281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.9253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9198e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9170e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.8985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.8959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.6093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2659 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 76/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.3159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 7.2871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.1656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.9820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.8189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.6847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.6525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.6247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.5561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.5494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.5340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4720e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.2738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.2664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.2595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.2544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.2487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.2428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.2368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.2313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.2266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.2265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.1959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.1929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.1899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.1550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.1515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.1480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.1135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.1105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.1075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.1046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.1016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.7962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2698 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 77/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.1848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.3967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.4629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.4103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.3577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.3006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.2605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.2198e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.2007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.2112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.2334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.2438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.2447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.2446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.2449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.2457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.2463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.2375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.2360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.2343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.2323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.2303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.2284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.2184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.2162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.2138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.1989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.1970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.1950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.1929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.1554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.1543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.1532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.1520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.1508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.1496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.1485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.1474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.1462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.1449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.1435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.1422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.1408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.9568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2739 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 78/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.7310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.9055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.9510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.1842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.2267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.2161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.2179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.3245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.3505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.3687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.3761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3309e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.2422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.2338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.2258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.2175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.1960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.1890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.1814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.1703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.1646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.1583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.1519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.1460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.1397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.1337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.1282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.1228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.1173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.1117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.1061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.1005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.0951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.0893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.0837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.0783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.0734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.0687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.0645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.9968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.9927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.9759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.9719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.9679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.9640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.9600e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.9560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.9521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.9448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.9411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.8996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.8963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.8931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.8899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.8867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.8836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.8806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.8777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.8747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.8718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.8690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.8662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.8634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.8605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.8577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.8550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.5229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2771 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 79/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.6916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.6873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.6186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.4888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.3854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.2960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.1066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.1072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.1079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.1081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.1083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.1086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.1087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.1085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.1083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.1080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.1075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.1070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.1065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.1061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.1049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.1041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.1035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.1032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.1028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.1023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.1018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.1012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.1006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.1004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.1004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.1002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.0998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.0995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.0992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.0993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.0991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.0991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.0992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.0993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.0995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.0999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.1000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.0998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.0995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.0957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.0954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.0951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.0152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2801 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 80/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.7729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.7242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.5729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.5400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.5381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.5360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.5312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.5322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.5334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.5342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5437e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2827 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 81/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.5099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.3716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3922e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3815e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2864 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 82/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.2605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1725e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.1686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2887 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 83/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.2323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.1773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2685e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.1441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.1426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.1410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.1395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.1379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.1364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.1349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.1334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.1323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.1312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.1301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.1288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.1276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.1263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.1250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.1238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.9461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2898 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 84/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.6301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.6462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.6511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6834e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.7156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.7157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.7159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2925 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 85/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.7895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7456e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.7142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.6653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.6140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.5785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.5090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.5067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.5039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.5022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4839e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2953 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 86/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.6477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.6251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.6058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.5880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.5706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.5542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.5391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.4902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.4804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.4714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.4630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3805e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.3623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.7258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.0930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.8916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.3128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.7542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.3028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.4985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0903e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1618e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2361e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3138e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3984e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4865e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5844e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7924e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9056e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0278e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1584e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2950e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4376e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7535e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1103e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3073e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5105e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.9408e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.1739e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4239e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.6839e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9999 - loss: 3.5615e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9762 - val_loss: 0.2661 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 87/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 - val_accuracy: 0.9716 - val_loss: 0.2138 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4965 Epoch 88/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9986 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9986 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9985 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9985 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988 - val_accuracy: 0.9749 - val_loss: 0.2096 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4974 Epoch 89/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9989 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9992 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9992 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9997 - loss: 7.7462e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 - val_accuracy: 0.9769 - val_loss: 0.2285 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4983 Epoch 90/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.2445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.2308e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.2630e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.2294e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.1947e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.3051e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.3646e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.3920e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4593e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4760e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.4838e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.4881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.4896e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.4884e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.4828e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.4747e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.4653e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4553e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4444e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4328e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4216e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4012e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3910e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3812e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3717e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3617e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3517e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3434e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 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━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2966e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2921e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2873e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2825e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2775e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 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━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.2018e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1968e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1919e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1822e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1725e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1677e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1629e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1584e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1539e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1494e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1450e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1405e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1361e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1318e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1275e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1232e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1189e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1147e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1105e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1063e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0980e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0939e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0899e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0859e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0819e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0780e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0741e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0702e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0624e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0586e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0547e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0509e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0471e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0433e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0396e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0322e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0248e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0212e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0175e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0139e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0103e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0067e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0031e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9995e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9960e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9890e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9822e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9685e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9619e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9586e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5651e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2420 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 91/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1824e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1832e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1624e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1407e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1089e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0986e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0880e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0820e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0775e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0725e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0684e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0656e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0637e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0608e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0546e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0513e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0481e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0414e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0387e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0335e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0315e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0300e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0262e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0244e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0227e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0189e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0171e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0136e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0120e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0105e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0093e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0079e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0065e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0050e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0035e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0022e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0008e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.9948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.9826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.9700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.9578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.9462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.9353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.9240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.9126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.9011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.8902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.8795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.8683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.8574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.8470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.7960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.7856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.7751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.7652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.7564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.7471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6869e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.6787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.6704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.6620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.6211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.6133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.6057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.5982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.5905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.5830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.5753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.5676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.5599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.5520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.5442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.5365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.5288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.5211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.5136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.5061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.5002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.4942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.4880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.4820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.4758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.4695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.4632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.4571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.4509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.4448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.9181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2522 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 92/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.1340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.2488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.2795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.0891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.9102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.7764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.6826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.5908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.5146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.4214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.3884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.4703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.4722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.4739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.4756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.4770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.4762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.4741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.4724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.4707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.4707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.4688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.5000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.5013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.5016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.5015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.5013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.5011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.5008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.4999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.4983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.4962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.4942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.4914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.4884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.4860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.4832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.4803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.4774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.4746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.4713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.4677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.4638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.4601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.4563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.4522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.4482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.4444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.4403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.4362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.4324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.4288e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.3928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.3879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.3830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.3782e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.3734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.3684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.3633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.3580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.3527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.3475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.3423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.3371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.3321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.3269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.3219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.3169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.3119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.3068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.3016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.2964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.2912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.2860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.2807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.2754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.2702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.2649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.2596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.2544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.2492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.2440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.2387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.2333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.2281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.2228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.2174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.2122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.2069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.2017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.5801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2592 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 93/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.8989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.9833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.9802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.8452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.7393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.6513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.6269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.5896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.5588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.5415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.5237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.7265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.0168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.0526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.0838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.3485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.3506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.3523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.3290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.3254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.3215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.3175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.3134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.3090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.2745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.2701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.2658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.1971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.1929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.1887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.5672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2640 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 94/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.7157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.8927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.9936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.9320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.6847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.6155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.5963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.5849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.5720e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.5604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.5490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.5384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.5286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.4983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.5003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.5010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.5011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.5017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.5011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.4994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.4974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.4960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.4939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.4944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.4953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.4964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.4980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.4990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.4996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.4998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.4927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.4920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.4912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.4836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.4823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.4818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.4774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.4765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.4759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4725e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.4635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.3184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2686 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 95/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.3220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.3609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.3325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.2519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.1580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.0889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.0488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.0106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.9784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.9001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.8627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.8570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.8517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.8477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.8430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.8393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.8365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.8342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.8308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.8271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.7055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.7035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.7015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2741 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 96/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.4900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.4774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.5230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.4610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.4137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.4153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.4144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.4100e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.3966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.3824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.3425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.3339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.3250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.3278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.3285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.3266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.3252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.3256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.3249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.3245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.3248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.3255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.3249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.3234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.3212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.3189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.3165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.3132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.3099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.3074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.3057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.3039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.3025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.3014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.3001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.2984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.2966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.2950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.2934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.2916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.2899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.2885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.2868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.2851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.2836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.2821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.2804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.2784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.2766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.2748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.2656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.2640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.2511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.2500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.2488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.2105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.2096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.2087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.2079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.2071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.2062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.2054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.2015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.2007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.2001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.1098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2778 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 97/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.1103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.9579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.9552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.9529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.9662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.9717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.9766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.9810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.9878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.9907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.0005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.0034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.0060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.0206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.0221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.0233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.0247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0326e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.9066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2810 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 98/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.1082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.0092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.9753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.9043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.6963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.6587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.6272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.6069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.5552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.5699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5720e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5566e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.4820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2852 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 99/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.1681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.2299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.2029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.1743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.1050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0869e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.0911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.0908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.0905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.0899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.0893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.0738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2881 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 100/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.1725e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.1261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9600e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2910 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 101/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.7730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.8794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.9387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.9134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.8768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.8085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.7748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.7745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.7728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.7520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.7509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.7495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.7482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.7468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.7455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.7441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.7430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.7418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.7407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.7397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.7388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.7379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.7369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.7358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.7350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.7341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.7336e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.7287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.7280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.7273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.7265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.7257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.7250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.7243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.7236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.7230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.7224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.7219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.7213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.7206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.7199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.7194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.7189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.7184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.7178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.7173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.7167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.7162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.7156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.7153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.7149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.7145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.7141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.7137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.7133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.7128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.7123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.7119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.7114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.7109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.7105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.7101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.7079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.7075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.7071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.7067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.7064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.7060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.7041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.7037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.7033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.6509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2945 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 102/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8456e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8456e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2963 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 103/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.4347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.4028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.4025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.2984 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 104/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3007 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 105/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.3242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.3164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.3109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.3051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.4386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3017 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 106/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.2389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.4871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - 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1.3995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3800e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3725e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3326e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3100e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3005e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2806e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2720e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.1796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3055 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 107/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s[0m 295ms/step - accuracy: 1.0000 - loss: 1.2874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.2402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1834e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.1533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.1470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.1419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.1381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.1207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.1180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.1149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.1033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.1023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.1011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.0997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.0982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.0975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.0987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.0995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.1000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.1005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.1009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.1013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.1016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.1019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.1024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.1023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.1020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.1016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.1012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.1008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.1004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.1000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.0997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.0995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.0991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.0987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.0982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.0979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.0977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.0974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.0972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.0971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.0971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.0971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.0973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.0976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.0979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.0981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.0982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.0983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.0984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.0984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.0985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.0985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.0985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.0985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.0985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.0984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.0984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.0983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.0982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.0981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.0979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.0977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.0975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.0974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.0972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.0970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.0969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.0967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.0965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.0963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.0961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.0960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 1.0959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 1.0958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 1.0957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.0957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.0956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.0955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3077 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 108/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.9341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.9176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.9043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.8933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.8852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.8748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.8625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.8490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.8416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.8347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.8257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.8165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.8091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 9.8005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.7920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.7840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.7763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.7678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.7586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.7518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.7448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.7378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.7295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.7212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.7055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.7076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.7093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.7106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.7127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.7129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.7132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.7108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.7093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.7084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.7078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.7073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.7060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.7042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.7022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.7002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.6981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.6960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.6940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.6920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.6898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.6852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.6827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.6820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.6809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.6797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.6782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.6764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.6749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.6738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.6723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.6711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.6698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 9.6686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 9.6670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 9.6651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 9.6630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 9.6610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 9.6588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.6563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.6538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.6516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.6491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.6440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.6415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.6388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.6358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.6298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.6265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.6229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.6193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.6158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.6095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.6063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.6033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.6000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.5965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.5932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.5899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.5865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.5831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.5796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.5764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.5731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 9.1754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3116 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 109/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 9.4608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 9.5164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 9.4818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 9.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 9.2026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 9.0880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 8.9926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.0267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.0262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.0159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.0186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.0125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.0091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.0134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.0215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.0152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.0068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 8.9974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 8.9888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 8.9817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 8.9688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.9567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.9479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.9368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.9260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.9159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.8951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 8.8859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 8.8767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 8.8697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.8650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.8596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.8541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.8489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 8.8294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 8.8237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.8166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.8091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.8012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.7943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.7804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.7735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.7671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.7613e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 8.7301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 8.7269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 8.7252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 8.7239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 8.7230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 8.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 8.7210e-07 - 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0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.6786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.6804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.6820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.6834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.6849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.6878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.6904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.6928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.6953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.6975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.6996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.7019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.7040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.7064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.7086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 8.7107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 8.7128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 8.7146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 8.8886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3130 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 110/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.6951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.5709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.7437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.9825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.9493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.9154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.8823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.8495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.8166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.7085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.6877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.6684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.5872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.5694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.5512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.5330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.4981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.4810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.3852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.3694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.3531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.3372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.3221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.3070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.2512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.2376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.2241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.2106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.0994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.1071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.1158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.1240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.1654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.1789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.1915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.2038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.2157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.2273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.2774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.2860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.2942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3141 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 111/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.9935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.8857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.8381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.8087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.7806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.7521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.6791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.6529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.6257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.5978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.5693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.5365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.5090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.4854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.4612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.4387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 9.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 9.4113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 9.3970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 9.3806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 9.3638e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 8.8489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 8.8447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 8.8403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.8317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.8276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.8232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.8188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.8143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.8098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.8052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.8005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.7913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.7867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.7821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.7777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.7736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.7694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3170 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 112/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.6408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 8.1404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 8.1331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.9824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.8414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.7307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.6726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 8.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.6058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.8586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.0378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.1697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.2862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.3692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.4281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.4629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.4806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.4884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.4875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.4627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.4423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.4221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.3978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.3726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.3496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.3278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.3050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.2820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.2570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.2319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.1820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.1324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.1073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.0845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.0624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.0406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.0182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.9949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.9729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.8880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.8679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.8475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.8275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.8083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.6834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.6664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.6505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.5909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.5769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.5627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.5491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.5356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.5221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.5086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.4953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.4824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.4698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.4578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.4464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.4357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.4250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.4050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.3951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.3853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.3752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.3558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.3462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.3368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 8.3275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 8.3184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 8.3094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 8.3007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 8.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 8.2831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 8.2743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 8.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 8.2575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 8.2494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 8.2412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.2253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.2187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.2119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.2030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.1987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.1944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.1899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.1856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.1813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.1769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.1727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.1687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.1608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.1485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.1443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.1415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.1386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.1359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.1330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.7893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3192 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 113/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 7.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.4029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 7.5185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 7.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 7.3996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 7.3210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 7.2540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.2574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 7.2576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 7.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.2646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 7.2643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 7.2789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 7.2955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.2906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.2830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - 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7.2075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 7.2024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 7.1943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 7.1846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 7.1751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 7.1693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 7.1661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 7.1605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 7.1563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 7.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 7.1538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 7.1522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 7.1506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 7.1496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 7.1477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 7.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 7.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 7.1405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 7.1388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 7.1362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 7.1333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 7.1305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 7.1269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 7.1275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 7.1279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 7.1277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 7.1333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 7.1381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 7.1419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 7.1451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 7.1475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 7.1495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 7.1513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 7.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 7.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.1551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.1592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.1629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.1735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.1779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.1838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.1891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.1941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 7.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 7.2037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 7.2079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 7.2118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 7.2156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 7.2192e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.2352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.2359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.2365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.2352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.2337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.2328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.2319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.2310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.2299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.2286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.2271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.2255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.2242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.2212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.2197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.2181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.2167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.1088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3208 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 114/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.2058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.2644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.4001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.3411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.3283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.3137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.2745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.2784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.2934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.4474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.4471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.4466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.4460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.4452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.4443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.4431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.4421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.4409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.4400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.4388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.4376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.4363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.4221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.4203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.4182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.4165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.4134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.4117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.4079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.4059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.4037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.4015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.0792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3239 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 115/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.3165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.5543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.6112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.5510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.5265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.5086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.4988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.5030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.4916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.4830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.5190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.5410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.5987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.6044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.6062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.6060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.6043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.5979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.5910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.5844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.5818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.5788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.5750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.5718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.5684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.5645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.5612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.5584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.5563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.5532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.5501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.5478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.5446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.5410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.5401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.5386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.5359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.5331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.5309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.5287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.5258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.5231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.5212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.5187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.5172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.5143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.5123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.5100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.5086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.5079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.5071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.5059e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.4935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.4928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.4921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.4914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.4905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.4894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.4881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.4867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.4854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.4839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.4823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.4809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.4801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.4794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.4787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.4773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.4765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.4755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.4745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.4745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.4749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.4753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.4759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.4761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.4768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.4771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.4773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.4778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.4782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.4786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.4788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.4789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.4792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.4793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.4794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.4793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.4790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.4792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.4793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.4858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3272 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 116/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.5246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.2357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.7818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.9081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.7463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.5793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.3094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.0131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.9360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.7895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.7209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.6584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.6021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.5486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.7983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.7904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.7827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.6622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.6581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.6541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.6460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.6419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.6380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.6338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.6314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.6293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.3154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3294 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 117/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.8449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.5920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.8435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.9697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.9342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.8033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.6541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.4675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.4466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.4269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.4054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.3820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.3427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.3246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.2971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.2867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.2760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.5047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.5228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.5391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.5529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.5778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.5893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.5987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.6068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.6139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.6209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.6267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.6320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.6368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.6406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.6527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.6661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.6788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.6905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.7020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.7125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.7227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.7322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.7410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.7493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.7568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.7639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.7708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.7828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.7883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.7933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.7979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.8022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.8062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.8127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.8156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.8182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.8204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.8233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.8260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.8283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.8302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.8321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.8338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.8351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.8362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.8419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.8424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.7102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3316 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 118/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 9.3255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 8.2952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 7.7781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.4922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.2196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.0060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.8254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.6897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 6.5499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 6.4366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.3391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.2504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 6.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 6.1031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 6.0421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.9812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.9208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.8619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.8062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.7569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.7089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.6636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.6235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.5836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.5472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.5139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.4838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.4565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.4300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.4045e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.5933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.5917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.5899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.5882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.5865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.5848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.5936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.6021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.6102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.6180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.6254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.6327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.6398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.6533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.6596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.6658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.6717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.6829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.6881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.6931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.6979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 6.2282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3312 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 119/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 5.3943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.2719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.3250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.1958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.0638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.9792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.8622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.8054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.7683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.7440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.7148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.6584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.6476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.6394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.6295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.6171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.6057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.5672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.5320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.5279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.5247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.5211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.5594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.5955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.6293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.6599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.6876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.9076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.9172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.9267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.9362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.9449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.9534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.8381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3346 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 120/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.1416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.0662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.9807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.8773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.6614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.5020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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4.4027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.3962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.3895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.3815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.3113e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.2022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.1999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.1976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.1954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.1932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.1910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.1889e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.1771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.1780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.1813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.2000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.2020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.2040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.2059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.2076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.2093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.2111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.2129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.2198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.2208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.2218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.2228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.2237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.2246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.2912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3365 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 121/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.5219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.4480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.4061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.3120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.2276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.2943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.3122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.3814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.4645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.4812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.4958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.4632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.4519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.4407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.4258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.4182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.4092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.3998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.3901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.3802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.3697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.3593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.3499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 4.3399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 4.3305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 4.3240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 4.3178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 4.3111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 4.3046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 4.2981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 4.2916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 4.2850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 4.2780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 4.2715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 4.2655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 4.2593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 4.2532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 4.2473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 4.2415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 4.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 4.2295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 4.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 4.2174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 4.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 4.2054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.1995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.1939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.1887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.1835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.1786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.1688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.1637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.1586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.1538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.1499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.1457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.1416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.1376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.1335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.1298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.1262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.1190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.1153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.1116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.1078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.0960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.0767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.0736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.0705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.0643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.0612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.0581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.0552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.0522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.0493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.0465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.0436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.0409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.0380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.0358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.0336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.0314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.0276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.0257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.0239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.0222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.0144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.0129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.0114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3394 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 122/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.8468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.3518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.6124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.5934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.5089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.4258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.3461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.2080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.1483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.0997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.0658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.0731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.0805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.0761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.0680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 4.0521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 4.0436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 4.0341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.0252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.0461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.0715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.0933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.1141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.1308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.1691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.1809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.1901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.1976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.2044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.2101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.2493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.2487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.2479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.2419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.2404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.2371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.2342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.2325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.2310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.2278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.2261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.2243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.2224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.2205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.2166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.2145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.2121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.2077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.2054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.2028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.2002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.1975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.1948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.1921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.1893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.1866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.1764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.1740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.1617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.1594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.1575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.1480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.1460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.1441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.1404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.1386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.1349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.1330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.1311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.1291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.1272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.1251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.1231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.1210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.1190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.8685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3408 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 123/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.5152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.5415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.5437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.7048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.7091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.7131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.7307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.7334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.7356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.7376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.7408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3420 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 124/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.0350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.7798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.7075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.6337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.5787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.5509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.5110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.4575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.4510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.4460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.4516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.4642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.4702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.4994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.1412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3450 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 125/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.7958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.2319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.2517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.2531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.2576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2932e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.2943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.2948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.2961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.2963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.2964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.2964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.2964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.2964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.2964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3457 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 126/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.5689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.8552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.4919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.2076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.8453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.7267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.6261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.5374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.4662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.4175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.3103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.2727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.2694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.2670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.2643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.2678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.2702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.3474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3467 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 127/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.6042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.8586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.5300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - 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3.1392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.0869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3491 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 128/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.7739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.7407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.7995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.7950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.7312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.7330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.7348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.7364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.7381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.7397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.7416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.7454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.9567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3499 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 129/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.4837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.5454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.5486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.5005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.5029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.5069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.5083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.5096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.5108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.5122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.5139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.5150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.5155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.5159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.5154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.5149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.5155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.5156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.5156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.5157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.5156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.5150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.5145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.5140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.5138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.5134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.5130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.5127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.5125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.5121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.5135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.5147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.5159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.5169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.5177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.5184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.5190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.5196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.5200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.5203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.5205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.5207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.5208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.5211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.5212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.5215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.5217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.5242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.5266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.5289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.5311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.5330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.5349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.5398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.5413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.5426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.5439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.5451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.5461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3529 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 130/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.3845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.2468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.2478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.2709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3555 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 131/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.9285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.2703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.2875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.2244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.1632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.5917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.5798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.5678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.5217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.5105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.5000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.4902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.4809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.4725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.4649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.4573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.4499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.4428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.4359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.4289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.4217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.4147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.4081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.4014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.3967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.3922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.3879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.3840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.3809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.3778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.3749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.3719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.3687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.3624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.3593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.3561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.3530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.3429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.3411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.3393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.3375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.3358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.3339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.3324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.3308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.3292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.3278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.3264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.3250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.3181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.3167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.3152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.3127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.3102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.3079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.3067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.3054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.3040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.3013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.2999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.2986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.2973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.2906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.2893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.2868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.2855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.2843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.2785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.2773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.2761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.2750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.2747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3588 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 132/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.0854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.8606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.8636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.9215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.9220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.9227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.9238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.9243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.9250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3603 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 133/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 2.2570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.1510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.1125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.0812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.9986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.9782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.9573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.9396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.9268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.9178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.8990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.8942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.8897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.8893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.8885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.9136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.9161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.9178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.9186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.9201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.9228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.0008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3625 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 134/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.2634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.1485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.0694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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2.4812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.4226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.4171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.4060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3587e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.3290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.3260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.3231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.3204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.3175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.3117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2942e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.2911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3625 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 135/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.0060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.9790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.9284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.7609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.7798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.7804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.7811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.7818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.7824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.7830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.7835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.7892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.7946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.7999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.2090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.8612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.9673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.8624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.9762e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9747 - val_loss: 0.3992 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 136/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9982 - loss: 0.0067 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9966 - loss: 0.0153 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9962 - loss: 0.0171 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9958 - loss: 0.0188 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9953 - loss: 0.0209 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9949 - loss: 0.0228 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9945 - loss: 0.0242 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9942 - loss: 0.0255 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9938 - loss: 0.0267 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9935 - loss: 0.0279 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9931 - loss: 0.0290 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9928 - loss: 0.0300 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4979  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9924 - loss: 0.0309 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4977  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9921 - loss: 0.0318 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4975  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9918 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4973  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9915 - loss: 0.0333 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4970  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9913 - loss: 0.0339 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4969  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9910 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4967  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9908 - loss: 0.0350 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4965  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9906 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4963  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9904 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4961  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9902 - loss: 0.0362 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4960  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9900 - loss: 0.0366 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4958  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9898 - loss: 0.0369 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4957  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9897 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4955  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9895 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4954  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9894 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9892 - loss: 0.0380 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4951  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9891 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4950  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9890 - loss: 0.0384 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4949  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9889 - loss: 0.0385 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4948  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9888 - loss: 0.0387 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4947  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9887 - loss: 0.0388 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4946  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9886 - loss: 0.0388 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4945  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9885 - loss: 0.0389 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4944  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9885 - loss: 0.0390 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4943  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9884 - loss: 0.0390 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9884 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4942  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9883 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4941  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9883 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9882 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4940  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9882 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4939  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9882 - loss: 0.0391 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4939  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9881 - loss: 0.0390 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9881 - loss: 0.0390 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4938  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9881 - loss: 0.0389 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9881 - loss: 0.0389 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9881 - loss: 0.0388 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9881 - loss: 0.0388 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9880 - loss: 0.0387 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9880 - loss: 0.0386 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9880 - loss: 0.0385 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9880 - loss: 0.0385 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9880 - loss: 0.0384 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9880 - loss: 0.0383 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9881 - loss: 0.0382 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9881 - loss: 0.0381 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9881 - loss: 0.0380 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9881 - loss: 0.0379 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9881 - loss: 0.0378 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9881 - loss: 0.0377 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9881 - loss: 0.0376 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9881 - loss: 0.0375 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9882 - loss: 0.0373 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9882 - loss: 0.0372 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9882 - loss: 0.0371 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9882 - loss: 0.0370 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9883 - loss: 0.0369 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9883 - loss: 0.0368 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9883 - loss: 0.0366 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9883 - loss: 0.0365 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9884 - loss: 0.0364 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9884 - loss: 0.0363 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9884 - loss: 0.0361 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9884 - loss: 0.0360 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9885 - loss: 0.0359 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9885 - loss: 0.0358 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9885 - loss: 0.0357 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9886 - loss: 0.0355 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9886 - loss: 0.0354 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9886 - loss: 0.0353 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9887 - loss: 0.0352 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9887 - loss: 0.0350 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9887 - loss: 0.0349 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9887 - loss: 0.0348 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4933  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9888 - loss: 0.0347 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9888 - loss: 0.0345 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9888 - loss: 0.0344 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9889 - loss: 0.0343 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9889 - loss: 0.0342 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9889 - loss: 0.0341 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9890 - loss: 0.0339 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9890 - loss: 0.0338 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9890 - loss: 0.0337 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9891 - loss: 0.0336 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9891 - loss: 0.0335 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4934  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9891 - loss: 0.0333 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9892 - loss: 0.0332 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9892 - loss: 0.0331 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9892 - loss: 0.0330 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9893 - loss: 0.0329 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9893 - loss: 0.0328 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9893 - loss: 0.0326 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9894 - loss: 0.0325 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4935 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9894 - loss: 0.0324 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9894 - loss: 0.0323 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9895 - loss: 0.0322 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9895 - loss: 0.0321 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9895 - loss: 0.0320 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9895 - loss: 0.0319 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9896 - loss: 0.0317 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9896 - loss: 0.0316 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9896 - loss: 0.0315 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4936 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9897 - loss: 0.0314 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9897 - loss: 0.0313 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9897 - loss: 0.0312 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9898 - loss: 0.0311 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9898 - loss: 0.0310 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9898 - loss: 0.0309 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9899 - loss: 0.0308 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4937 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9934 - loss: 0.0187 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4952 - val_accuracy: 0.9761 - val_loss: 0.1790 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4971 Epoch 137/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9978 - loss: 0.0061 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9978 - loss: 0.0059 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9979 - loss: 0.0057 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4981  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9980 - loss: 0.0055 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4982  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9981 - loss: 0.0053 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9981 - loss: 0.0052 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9981 - loss: 0.0052 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9981 - loss: 0.0051 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4983  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9982 - loss: 0.0051 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9982 - loss: 0.0051 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9982 - loss: 0.0050 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4984  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9982 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9982 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9983 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9983 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9983 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9984 - loss: 0.0046 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9984 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9984 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989 - val_accuracy: 0.9767 - val_loss: 0.1996 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4978 Epoch 138/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.8679e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.9337e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.9142e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.8462e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.7949e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.7787e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.7624e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7448e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7199e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.6926e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.6639e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.6323e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.6022e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5737e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5460e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5167e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.4871e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.4580e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4298e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4023e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3757e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3502e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.3266e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.3035e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.2818e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.2607e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.2403e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.2200e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.1999e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.1802e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.1609e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.1420e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.1233e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.1051e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.0875e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0701e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0533e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0369e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0210e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0052e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9898e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.9746e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.9598e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.9453e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.9310e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.9170e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.9034e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8771e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8644e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8520e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8397e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8277e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8159e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8043e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.7929e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7817e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7707e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7599e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7493e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7389e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7287e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7188e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7089e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6993e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6898e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6804e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6712e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6621e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6532e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6445e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6359e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6274e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6191e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6109e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6028e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5949e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5870e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5716e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5641e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5567e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5493e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5421e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5350e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.5280e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.5212e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.5143e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.5076e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5010e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4944e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4880e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4816e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4753e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4691e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4630e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4570e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4510e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4452e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4394e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4336e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4279e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4223e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4168e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4113e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4058e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4005e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3952e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3900e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3848e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3797e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3746e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3696e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3646e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3597e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3549e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3501e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3453e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3406e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3360e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.8145e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9769 - val_loss: 0.2232 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4983 Epoch 139/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.3254e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.2280e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.2092e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.0927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9930e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8562e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8040e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.7532e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.7136e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.6897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.6648e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.6483e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.6369e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6298e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6183e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6057e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5807e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5684e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5555e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5478e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5418e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5296e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5256e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5228e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5186e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5138e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5086e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5035e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4982e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4821e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4770e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4728e-05 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2797e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2764e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2731e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2697e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2630e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2596e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2562e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2529e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2496e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2464e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2432e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2401e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2369e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2337e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2305e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2178e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.2146e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.2115e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.2084e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2053e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2023e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1992e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1961e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1930e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1899e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1837e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1776e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1745e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.8100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2375 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 140/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.5072e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.5095e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.5547e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5313e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4937e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4566e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4329e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4086e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3851e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3669e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3550e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3432e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3260e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3203e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3138e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3069e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3007e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2956e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2899e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2813e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2781e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2754e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2733e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.2719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.2696e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2655e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2607e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2553e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2531e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2506e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2484e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2464e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2458e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2447e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2435e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2422e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2411e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2398e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2366e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2353e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2341e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2329e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2318e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2308e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2296e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2284e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2270e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2256e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2226e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2211e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2198e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2183e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2158e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2146e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2103e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2089e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2074e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2058e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2028e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2013e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1999e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1993e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1986e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1968e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1959e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1949e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1939e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1916e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1894e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1882e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1871e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1860e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1838e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1826e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1814e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1803e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1790e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1778e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1767e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1755e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1731e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1707e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1694e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1669e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1656e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1629e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1616e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1602e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1589e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1576e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1563e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1550e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1537e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1523e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1510e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1497e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1484e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1470e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1457e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1444e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9884e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2458 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 141/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8846e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.8865e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.8893e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.8533e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.8218e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7926e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.7710e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7514e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.7309e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7161e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7068e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6971e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6912e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6866e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6837e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6793e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6864e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6916e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6955e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6978e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6988e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7003e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7005e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7009e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7013e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7019e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7017e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7011e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7003e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6996e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6986e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6973e-05 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6351e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6339e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6328e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6317e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6305e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6294e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6283e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6272e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6261e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6250e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6238e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6227e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6216e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6204e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6192e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6181e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6159e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6149e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6138e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6128e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6107e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6096e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6079e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6070e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6061e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6052e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5005e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2528 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 142/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.5774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5256e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5332e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5172e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4932e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4713e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4528e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4614e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4621e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4584e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4541e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4504e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4473e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4450e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4414e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4372e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4325e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4279e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4291e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4287e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4289e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4295e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4295e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4295e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4291e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4287e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4279e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4267e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4255e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4243e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4230e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4216e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4205e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4196e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4183e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4169e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4153e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4137e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4120e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4102e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4084e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4075e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4065e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4045e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4036e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4025e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4014e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4003e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3991e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3979e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3967e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3955e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3943e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3931e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3920e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3910e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3889e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3878e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3866e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3844e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3832e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3821e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3810e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3798e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3776e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3766e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3755e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3743e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3732e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3721e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3697e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3686e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3675e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3654e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3644e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3634e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3623e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3613e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3602e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3592e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3581e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3570e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3560e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3549e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3539e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3528e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3518e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3508e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3487e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3477e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3466e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3456e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3445e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3435e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3424e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3414e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3404e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3394e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3384e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3375e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3365e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3356e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3346e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3337e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3327e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3318e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3309e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3299e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.2195e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.2587 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 143/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2402e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2078e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1979e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1354e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1136e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1013e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0896e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0687e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0633e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0577e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0543e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0523e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0513e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0467e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0460e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.0454e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.0446e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.0434e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.0175e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0172e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0164e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0161e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0157e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0151e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0148e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0145e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0139e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.0132e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.0128e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0124e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0114e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0111e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0108e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0104e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0101e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0094e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0086e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0082e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0078e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0074e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0067e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0064e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0060e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0057e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0055e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0054e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0052e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0050e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0046e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0044e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 9.8108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2633 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 144/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 9.7896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 9.8325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 9.8766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.6705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.4773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.3115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.2003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.1048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.0126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.9435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.9124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.8877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.8887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.8956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.8890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.8820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.8719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.8631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.8511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.8362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.8243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.8155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.8045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.7968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.7919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.7902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.7844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.7773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.7690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.7604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.7505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7405e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.7106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.7061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.7032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.7017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.6941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.6909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.6868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.6826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.6790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.6749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.6713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.6678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.6649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.6612e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.6572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.6529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.6513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.6508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.6499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.6489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.6483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6437e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.6366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.6355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.6343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.6333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.6324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.6317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.6306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.6294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.6278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.6261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6198e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.3564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.2666 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 145/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.5852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 8.3003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.1873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.2819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.2235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.0390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.9857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.9293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.8738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.8326e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.7176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.7128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.6962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.6766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.6532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.6301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.5750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.5496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.5273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.5053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.4588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.4441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.4296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.4140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.3147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.3031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.2928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.2812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.2690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.2567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.2445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.2324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.2196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.2071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.1956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.1843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.1741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.1642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.1550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.1451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.0953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.0853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.0760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.9955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.9895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.9831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.9770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.9709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.9652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.9592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.9531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.9468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.9405e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.9093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.9033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.8975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.8351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.8297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.8245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.8192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.8140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.8088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.8036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.1386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2713 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 146/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.2294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.1327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.1499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.9741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.8187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.6872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.6213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.3838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.3801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.3766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.3700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.3649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.3625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.3644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.3674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.3701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.3728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.3737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.3751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.3754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.3751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.3741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.3648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.3632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.3621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.3615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.3596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.3571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.3540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.3516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.3489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.3476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.3462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.3467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.3468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.3476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.3489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.3507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.3518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.3525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.3528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.3532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.3529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.3521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.3510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.3501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.3489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.3480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.3473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.3469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.3476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.3481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.3483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.3485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.3483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.3478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.3472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.3470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.3466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.3464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.3484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.3505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.3521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.3535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.3548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.3560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.3570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.3577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.3583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.3589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.3592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.3595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.3599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.3604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.3606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.3606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.3605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.3603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.3599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.3593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.3587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.3582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.3575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.3569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.3564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.3559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.3552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.3544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.3536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.3527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.3517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.3506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.3495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.3484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.3473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.3462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.3452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.3442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.3431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.3436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.3439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.3442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.3444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.3445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.3446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.3448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.3449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 6.3607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2755 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 147/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 6.2098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 6.1086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 6.1179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 5.9841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.8578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.7527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.6805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.6156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.5575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.5203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.5013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.4826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.4729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.4673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.4649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.4566e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.4471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.4388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.4313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.4228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.4142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 5.4071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 5.4024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 5.3975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 5.3946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 5.3928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 5.3920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 5.3899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 1.0000 - loss: 5.3880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 1.0000 - loss: 5.3861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 1.0000 - loss: 5.3843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 5.3849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 5.3849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 5.3852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 5.3862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 5.3867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 5.3958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 5.4045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 5.4130e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 5.4200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 5.4259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 5.4309e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 5.4356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 5.4392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 5.4452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 5.4509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.4564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.4612e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.4661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.4708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.4754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.4794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.4829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.4860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.4889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.5011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.5036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.5067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.5101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.5136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.5165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.5192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.5216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.5239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.5258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.5274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.5290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.5307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.5322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.5419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.5423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.5426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.5449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.5452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.5454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.5437e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.5434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.5432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.5415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.5417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.5419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.5427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.5432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.5439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.5465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.5467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.5469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.5425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.2776 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 148/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.1868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 1.0000 - loss: 5.2527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.3419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.5238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.5605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.5874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.5862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.5677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.5325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.5031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.4956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 5.4833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 5.4741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 5.4687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.4660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.4547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.4448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.4337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.4230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.4138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 5.4026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 5.3923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 5.3842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.3760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.3701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.1174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.1159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.1142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.1124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.1105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.1088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.1070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.1053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.1037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.1004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.0986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.0968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.0950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.0931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.0912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.0893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.0874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.0856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.0838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.0821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.0805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.0788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.0770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.0752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.0735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.0716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.0697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.0678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.0659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.0640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 4.8376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2817 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 149/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.1805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.1848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.1611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.9047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.8121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.7532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.6952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.6787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.6661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.6910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.8074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.8178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.8238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.8279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.8390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.8443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.8493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 4.8529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.8565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.8599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.8697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.8721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.8737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.8752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.8795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.8830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.8864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.8897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.8929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.8953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.8973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.8988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.9001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.9010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.9014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.9018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.9022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.9023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.9024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.9026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.9029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.9028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.9024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.9019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.9014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.9010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.9004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.8998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.8992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.8983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.8967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.8959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.8948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.8935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.8921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.8907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.8846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.8714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8612e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.6082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2834 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 150/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.1590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.6891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.7367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.5883e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.4552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.3239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.2152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.0186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.9394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.7791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.7450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.1851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.1827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.1802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.1778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.1755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.1731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.1709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.1686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.1536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.1514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.1493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.1472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.1451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.1431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.1412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.1393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.1374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.1354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.1334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.1316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.1297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.1280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.1263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.1245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.1228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.1167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.1152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.1136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.1121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.1109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.1096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.1085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.1074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.9847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.2876 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 151/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.5036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.6089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.6596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.6337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.6146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.5872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.5462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.5490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.5533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.5598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.5613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.5605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5566e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.5595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.5721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.5739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.5760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.5785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.5803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.5814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.5826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.5925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.5961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.5992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.6031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.6072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.6113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.6148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.6180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.6210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.6237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.6365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.6393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.6420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.6577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.6597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.6615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.6704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.6717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.6730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6797e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.6818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.6827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.6836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.6844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.6854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.6862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.6869e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.6875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.6882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.6888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.9307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.2898 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 152/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.6003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.6081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.6297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.5495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.4077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.3818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.3593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3815e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.4076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4130e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.4141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.4129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.4127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.4219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.4305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.4369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.4428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.4479e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.4559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.6010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.6145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.6270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.6389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.6492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.6588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.6674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.6752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.6820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.6879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.6936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.6992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.7043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.7272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.7307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.7338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.7366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.7389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.7411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.7433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.7453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.7474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.7497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.7520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.7540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.7558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.7573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.7587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.7601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.7611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 3.7622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 3.7632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 3.7643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 3.7653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 3.7663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 3.7673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.7680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.7685e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.7689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.7693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.7694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.7694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.7693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.7694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.7694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.7694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.7694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.7694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.7692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.7688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.7684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.7679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.7673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 3.7667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 3.7660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.7654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.7650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.7645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.7641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.7636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.7602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.7593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.7584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.7575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.7565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.7555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.7501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.7489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.7477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.5824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.2918 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 153/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.5487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.2208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.1193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.1176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.1154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.1131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0437e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.2943 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 154/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.3858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.3161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.2687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.2293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.3940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.4563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.5951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.8494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.8464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.8433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.3054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.2967 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 155/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.4988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7869e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7675e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.3969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9768 - val_loss: 0.3003 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 156/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.7091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.6367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.5685e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.5132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.4674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.4293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.3071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.3021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.2983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.2931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.2874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.2811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.2750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2130e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.6195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.3028 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 157/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.2814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.2715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.2640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.2575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.2490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.2409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.2324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.2248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1234e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1238e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.1556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.3050 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 158/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.0523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.0674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9725e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.3056 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 159/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.8289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0479e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.3070 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 160/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 2.5561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.1458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.9701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.4255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.7250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.9842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.1688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.2994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.3954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.4638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.5443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.5640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.4873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.4724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.4572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.4416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.4259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.4103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.3950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.3799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.3652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.3508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.3366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.9193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.9112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.9032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8797e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.8358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.8288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.7948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.7882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.7817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.7753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.7690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.7628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7566e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.7152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.7096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.7040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9769 - val_loss: 0.3102 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 161/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.6234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.5944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.9190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.9796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.0020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.0208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.0431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.0626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.0837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.2145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.2483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.4132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.5102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.5986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.0539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.3581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.6355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.0273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.9794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.6963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.3737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.1149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.8247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.5886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.3963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.0235e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1134e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.2100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.3491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.4971e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.6604e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.8312e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.0050e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.1949e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3928e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.6224e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.8707e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.1477e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.4616e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.8329e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.2468e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.7137e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.2257e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.7783e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.3699e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.9803e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 7.6327e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 8.3367e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.0701e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.8344e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0653e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1527e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2448e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3402e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4409e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5464e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.6545e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9999 - loss: 1.7638e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9999 - loss: 1.8763e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9999 - loss: 1.9903e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9999 - loss: 2.1068e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9999 - loss: 2.2244e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9999 - loss: 2.3436e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9999 - loss: 2.4649e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9999 - loss: 2.5922e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9999 - loss: 2.7195e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9999 - loss: 2.8473e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9999 - loss: 2.9784e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9999 - loss: 3.1137e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9999 - loss: 3.2488e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9999 - loss: 3.3844e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9999 - loss: 3.5238e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9999 - loss: 3.6642e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9999 - loss: 3.8031e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9999 - loss: 3.9422e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9999 - loss: 4.0838e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9999 - loss: 4.2272e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9999 - loss: 4.3699e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9999 - loss: 4.5135e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9998 - loss: 4.6604e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9998 - loss: 4.8070e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9998 - loss: 4.9515e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9998 - loss: 5.0966e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9998 - loss: 5.2425e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9998 - loss: 5.3876e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9998 - loss: 5.5310e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9998 - loss: 5.6734e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9998 - loss: 5.8164e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9998 - loss: 5.9590e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9998 - loss: 6.0997e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 - val_accuracy: 0.9751 - val_loss: 0.2202 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4975 Epoch 162/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 0.9981 - loss: 0.0048 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9981 - loss: 0.0049 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9982 - loss: 0.0047 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4985  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9983 - loss: 0.0045 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9983 - loss: 0.0044 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4986  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9984 - loss: 0.0043 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9984 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9985 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4987  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9985 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9985 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9986 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9986 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4988  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9986 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9987 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9987 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9987 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4989  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9989 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9989 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9989 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9990 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 - val_accuracy: 0.9751 - val_loss: 0.2563 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4980 Epoch 163/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9992 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  59/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 - val_accuracy: 0.9768 - val_loss: 0.2576 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 164/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.9633e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.7170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.6930e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3690e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.2415e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1395e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0594e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9772e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0062e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0222e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0206e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0130e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.9978e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.9771e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.9506e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.9202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.8872e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8538e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.8201e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.7856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.7517e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.7192e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.7294e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7355e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7389e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7393e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7360e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7304e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7247e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7076e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.6967e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.6848e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.6722e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.6587e-05 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5375e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5211e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5067e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4776e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4631e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4485e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4194e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4047e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3900e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3607e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3461e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3316e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3182e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3060e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2939e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2818e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2697e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2576e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2454e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2333e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2211e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1969e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1849e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1736e-05 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0112e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0010e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.9909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.9809e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.9709e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.9610e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.9513e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.9416e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9320e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9224e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9130e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.9036e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.8943e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.8851e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8759e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8668e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8577e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8488e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8398e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8310e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8222e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8135e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.7963e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7878e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7794e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7710e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7627e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7545e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7463e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7301e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7141e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7062e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.7666e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.2696 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 165/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.4746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.3376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.2516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.0880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.8874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.7458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.6128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.4867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.3587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.2888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.1930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.1596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.1363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.1139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.0855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.0519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.0180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.9835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.9946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.9970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.9968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.9958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.9902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.9841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.9773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.9702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.9594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.9466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.9322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.9182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.9033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.8862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.8345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.8237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.8132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.8012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.7884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.7769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.7650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.7526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.7395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.7268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.6902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.6303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.6178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.6051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.5922e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.5795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.5673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.4044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.3050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.1764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.1712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.1660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.1606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.1553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.1499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.1443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.1388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.1333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.1277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.4642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2796 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 166/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.9344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.9801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.0641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.9091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.7430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.5391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.4574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.2221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.2006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.0977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.0893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.0817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.0726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.0632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.9991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.9907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.9826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.9755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.9381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.9306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.9232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.9161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.9089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.9020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.2067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2864 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 167/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.3135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3782e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.4697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.0450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9883e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8130e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.7907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.7805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.7144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.7062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.6419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.6356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.6367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.6374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.6347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.6265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.6244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.6226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.6062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.6039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.6017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.5898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.5871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.5844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.5817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.5792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.5769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.5747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.5726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.5704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.5683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.5662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.5641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.5619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.5597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.5574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.5550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.5533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.5514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.5496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.5478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.5459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.5442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.5424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.5409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.5393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.5375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.5287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.5270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.5252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.5234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.5216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.5199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.5181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.5162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.5143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.5125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.5107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.5088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.5069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.5050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.5032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.5014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.4926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.4909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.4892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.4264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2900 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 168/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.2808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.4118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.1049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.1049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.1040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.1030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.1016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0170e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.0060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.0039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.0017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.9223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.9206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.9189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.9173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2958 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 169/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.6907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.6349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.4192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5170e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.5026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.5011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5100e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.5443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2988 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 170/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.2958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.2743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1600e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.1339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3017 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 171/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.0704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.0717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.5758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.7014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.7197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.6962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.5699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.5273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.4583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.4294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.4059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.3071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.2893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.2719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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2.1741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.1231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.1172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.1115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0612e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0527e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9596e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3050 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 172/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6834e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.6909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.6919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.6928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.6937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.6946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.6954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.6961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.6967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.6973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.6980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.6987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.6994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.7537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3081 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 173/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.8082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.6919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.6385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.5998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.5737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.5555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.5407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.4922e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.5014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.5023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.5036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.5045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.5054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.5058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.5066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.5072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.5078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.5080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.5076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.5073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.5075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.5074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.5075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.5074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.5079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.5079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.5077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.5074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.5071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.5072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.5071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.5070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.5070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.5070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.5070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.5070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.5070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.5068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.5064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.5061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.5058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.5054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.5050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.5045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.5042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.5038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.5033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.5029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.5025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.5020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.5013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.5008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.5001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.4995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.4987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.4979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.4972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.4964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.4957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.4950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.4943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.4936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.4927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.4919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.4911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.4903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.4895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.4887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.4850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.4842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.4835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.4799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.4792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.4784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.4199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3101 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 174/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2685e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.2657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1883e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2170e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.2077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3113 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 175/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.2010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.1208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.1195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1170e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0922e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0883e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0871e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3160 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 176/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.0868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.9935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.9923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0696e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3167 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 177/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.7662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 9.7725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 9.6677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.5350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.4582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.3253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.2434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.1822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.1569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.1261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.0912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.0882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.0687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.0595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.0500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.0372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 9.0000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 8.9841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.9762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.9685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.9595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.9500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.9431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.9390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.9356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.9328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.9301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.9288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.9263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.9253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.9241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.9190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.9181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.9166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.9195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 8.9235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 8.9280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 8.9315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.9343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.9365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.9389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.9430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.9438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.9446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.9447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.9443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.9434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.9427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.9421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.9412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.9405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.9402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.9395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.9386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.9378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.9370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.9355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.9345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.9337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.9328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.9324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.9320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.9320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.9327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.9332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.9334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.9335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.9335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.9331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.9327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.9324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.9321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.9607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.9645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.9682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.9719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.9755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.9791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.9949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.9976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.0001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 9.2335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3188 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 178/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.0519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.7014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.6353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.5665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.5129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.4703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.4331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.3917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.3497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.2665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.1945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.2280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3211 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 179/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.9583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.9607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.3121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.1803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.1964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.0641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.0037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.9185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.8904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.8706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.8506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.7663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.7117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.6453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.6338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.6241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.6164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.6054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.5930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.5276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.5168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.5057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.4982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.5032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.5079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.5126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.5826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.5832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.5844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.5851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.5858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.5862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.5865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.5866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.5866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.5867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.5865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.5863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.5861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.5858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.5467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3241 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 180/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.6282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.4032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.4302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.3953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.3165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.2323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.1606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.0848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 7.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.9469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.9081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.8706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.8447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.8230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 6.7180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 6.6978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 6.6766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 6.6603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 6.6488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 6.6361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 6.6247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 6.6152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 6.6081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 6.5994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.5910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.5824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.5754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.5772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.5773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.5771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.5787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 6.5799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 6.5806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 6.5820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 6.5840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 6.5845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 6.5844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 6.5840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 6.5832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 6.5830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 6.5820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 6.5812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 6.5845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 6.5874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 6.5908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 6.5944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 6.5983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 6.6010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 6.6035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 6.6059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 6.6083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 6.6102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 6.6117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 6.6128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 6.6140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 6.6148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 6.6155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 6.6162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 6.6170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 6.6175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 6.6177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 6.6178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 6.6178e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.6109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.6099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.6089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.6077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.6063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.6049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.6040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.6031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.6024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.6020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.6016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.6010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.6003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.5995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.5986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.5976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.5968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.5960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.5953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.5945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.5938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.5931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.5924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.5915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.5906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.5896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.5885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.5875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.5865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.5856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.5848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.5828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.5819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.5811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.5801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.5777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.5765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.5754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.5742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.5729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.5718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.5707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 6.4444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3260 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 181/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 7.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.2279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.4065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.3465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.9939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.4151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.6833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.8735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.9144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.9146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.9028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.8937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.8798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.8574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.8255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.7923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.7570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.7176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.5736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.5436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.5156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.4209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.3985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.3772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.2923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.2026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.1842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.0958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.0547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.0423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.0294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.0165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.0037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.3053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.3298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.3535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.3760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.3975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.4375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.4555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.4727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.4889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.5042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.5185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.5319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.5454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.5579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.5700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.5815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.5927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.6945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.7000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.7369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.7438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.3599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3239 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 182/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.2732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.3914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.6748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.7409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.7478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.6316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.1579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.4622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.6396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.7548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.9173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.9390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.9488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.9478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.9338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.9174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.8394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 8.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 8.7910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 8.7675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 8.7438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 8.7202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 8.6994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 8.6764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.6535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.6358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.6179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.5991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.5790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.5597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.5414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.5230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.5046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.4876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.4711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.4538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.4361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.4189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.3850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.3682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.2796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.2659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.0454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.0350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.0248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.0147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.9953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.9856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.9756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.9658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.9563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.9091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.9000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.8911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.7969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.7889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.7812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.7264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.6967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.6897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.6826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.6754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.6683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.6612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.6541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.7950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3265 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 183/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.4250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.2443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.1862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.9611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.9767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.0660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.5952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.7401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.8420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.9086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.9409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.9514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.9509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.9379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.7805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.7060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.6694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.6299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.5885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.5467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.5043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.4615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.3744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.3325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.2911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.2506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.5925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.5664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.5412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.5169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.4703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.4480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.4256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.4034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.9898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.9750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.9602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.9456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.9315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.9173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.9036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.8381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.8258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.8134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.8020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.7905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.7792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.7715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.7560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.7251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.7175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.7098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.7021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.6944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.6867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.6790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.7600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3309 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 184/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.2701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.6112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.7725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.6954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.6231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.6759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.2775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.8282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.9702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.8915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.8518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.8156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.6937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.6666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.6382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.5806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.5543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.5281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.4767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.4516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.4278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.3854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.3648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.3437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.3228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.3021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.2816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.2610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.2225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.2034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.1850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.1670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.1493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.1317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.1144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.0971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.0636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.0467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.0304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.0148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.8870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.8738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.8609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.8485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.8360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.8238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.8121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.8009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.7784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.7672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.7561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 6.7453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 6.7344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 6.7235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.7130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.7025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.6922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.6822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.6726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.6631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.6535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.6089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.6009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.5929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.5856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.5784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.5713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.5641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.5569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.5497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.5427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.5357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.5286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.5217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.5152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.5087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.5023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.4960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.4900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.4838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.4777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.4716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.4655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 6.4594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.4472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.4413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 6.4353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 5.7284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3312 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 185/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 5.0155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.9572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.0947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 5.0416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.0432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 5.0323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.0253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.0022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.9700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.9490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.9478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.9450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.9409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.9388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.9423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.9420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.9434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.9470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 4.9495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 4.9507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.9537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.4097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.4898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.4986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.5071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.5153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.5233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.5310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.5905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.5976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.4028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3317 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 186/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.9595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.1319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.2445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.1990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.1215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.0666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.0120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.9745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.9363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.9067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.8042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.7976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.7931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.7921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.7883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.7852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.7828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.7838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.7840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.7735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.7720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.7710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.7436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.7401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.7387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.7396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.7416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.7422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.7428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.7428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.7423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.7419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.7416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.7411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.7406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.7404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.7402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.7397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.7391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.7385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.7377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.7381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.7381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.7382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.7385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.7217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.7209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.7200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.6088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3345 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 187/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.5920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.2292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.1195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.6303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.8186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.7984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.7579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.7006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.6468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.5022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.2861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.1892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.1722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.1372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.1195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.0864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.0479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.0361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.0249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.0056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.8692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.9267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.9433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.9506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.9610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.0914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.0944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.0975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.1170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.1184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.1199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.1212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.1226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.1240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.1254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.1266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.1277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.1288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.1297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.1312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.1324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.1328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.1817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3374 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 188/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.9108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.8807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.8386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.8335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.0103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.0100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.9406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3392 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 189/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 6.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.6450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.4269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.1878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.9714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.6686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.5754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.4441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.3870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.3201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.2635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.2336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.2055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.1786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.1518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.1242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.0171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.9861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.9712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.9152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.9024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.8906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.8794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.8692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.8596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.8349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.8273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.8271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.8262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.8250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.8250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.8254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.8263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.8275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.8285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.8289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.8286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.8281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.8274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.8265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.8252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.8241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.8227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.8219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.8212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.8205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.8182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.8167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.8153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.8136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.8118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.8102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.8086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.8069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.8052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.8020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.7985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.7968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.7950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.7931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.7911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.7891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.7853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.7836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.7820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.7804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.7787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.7769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.7750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.7734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.7718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.7700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.7683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.7666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.7649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.7631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.7614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.7598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.7580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.7562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.7543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.7524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.7504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.7485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.7465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.7446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.7409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.4846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3408 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 190/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.4540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.1939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.1975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.2867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.2864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.1206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3434 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 191/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.9893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.0315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.9672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.9550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.9427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.9221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.9080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.6004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.6116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.6222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.6326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.6427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.6525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.6621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.6714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.6805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.6891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.6975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3429 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 192/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.3199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - 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3.3501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.3503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.3492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.3474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.3457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.3444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.3427e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.2188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.2167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.2145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.2017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.2006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.1996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.1985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.1960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.1948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.1953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.1957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.1961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.1966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.1969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.1976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.1980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.1982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.1983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.1984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.1986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.1987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.1989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3457 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 193/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 2.9847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.9827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.9402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.8965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.7904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.7763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.7685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.7668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.2808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.4300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.9128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.3241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.3884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.4480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.9043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.9267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.9862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.0728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.1674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.1796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.2024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.2189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.2265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.2773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.2897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.3013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.3125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.3232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.3334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.3432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.3698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.3779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.3854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.3927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.5371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.8138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.0753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.2005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.4407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.6680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.0522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.2162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.3503 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 194/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.0289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.2221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.7251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.1570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1769e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4727e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6938e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9647e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5912e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1732e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7856e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4060e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9778e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.4752e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.0331e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.5640e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1038e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8053e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6023e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8206e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1440e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3532e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5857e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8932e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9999 - loss: 2.2276e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9999 - loss: 2.5567e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9999 - loss: 2.9228e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 3.3243e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 3.7480e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 4.1897e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 4.6653e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9999 - loss: 5.1747e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 5.7333e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 6.3092e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 6.9276e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 7.5866e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 8.2509e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 8.9019e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 9.5712e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998   43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9993 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 - val_accuracy: 0.9761 - val_loss: 0.2925 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 195/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9998 - loss: 6.5752e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9998 - loss: 6.5158e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9998 - loss: 6.7127e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9998 - loss: 6.5565e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9998 - loss: 6.5018e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9998 - loss: 6.5121e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9998 - loss: 6.5430e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9998 - loss: 6.5316e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9998 - loss: 6.5179e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9998 - loss: 6.5186e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9998 - loss: 6.5094e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9998 - loss: 6.4804e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9998 - loss: 6.4556e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 6.4294e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 6.4039e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 6.3760e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9998 - loss: 6.3439e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9998 - loss: 6.3072e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9998 - loss: 6.2703e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.2373e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.2067e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.1767e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.1443e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9998 - loss: 6.1092e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9998 - loss: 6.0750e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9998 - loss: 6.0441e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9998 - loss: 6.0158e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9998 - loss: 5.9900e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9998 - loss: 5.9644e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.9368e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.9068e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.8760e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.8460e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 5.8191e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 5.7939e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 5.7681e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 5.7422e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 5.7158e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 5.6884e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 5.6599e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 5.6317e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 5.6059e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 5.5806e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9998 - loss: 5.5557e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9998 - loss: 5.5301e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9998 - loss: 5.5042e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9998 - loss: 5.4782e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9998 - loss: 5.4520e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9998 - loss: 5.4265e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 5.4027e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 5.3796e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 5.3562e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 5.3324e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 5.3084e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 5.2842e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 5.2600e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 5.2367e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 5.2144e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 5.1925e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 5.1706e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 5.1487e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 5.1268e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 5.1048e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 5.0827e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 5.0607e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 5.0403e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 5.0202e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 5.0002e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 4.9804e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 4.9605e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 4.9407e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 4.9208e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 4.9014e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 4.8824e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 4.8636e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 4.8449e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 4.8262e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 4.8077e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 4.7892e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 4.7706e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9999 - loss: 4.7521e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9999 - loss: 4.7336e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9999 - loss: 4.7152e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9999 - loss: 4.6968e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9999 - loss: 4.6785e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9999 - loss: 4.6603e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9999 - loss: 4.6422e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9999 - loss: 4.6242e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9999 - loss: 4.6062e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.5883e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.5705e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.5527e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.5351e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9999 - loss: 4.5175e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9999 - loss: 4.4999e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9999 - loss: 4.4825e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9999 - loss: 4.4651e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9999 - loss: 4.4478e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9999 - loss: 4.4307e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9999 - loss: 4.4136e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9999 - loss: 4.3965e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9999 - loss: 4.3796e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9999 - loss: 4.3628e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9999 - loss: 4.3460e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9999 - loss: 4.3294e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9999 - loss: 4.3128e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9999 - loss: 4.2964e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9999 - loss: 4.2800e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9999 - loss: 4.2638e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9999 - loss: 4.2476e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9999 - loss: 4.2315e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9999 - loss: 4.2156e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9999 - loss: 4.1997e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9999 - loss: 4.1840e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9999 - loss: 4.1683e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9999 - loss: 4.1527e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 4.1373e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 4.1219e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 4.1067e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 4.0915e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9999 - loss: 2.2883e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9769 - val_loss: 0.2792 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 196/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3427e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.3209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2959e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2557e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2184e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1880e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1628e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1408e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1193e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1019e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0883e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0773e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0686e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0619e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0560e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0438e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0380e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0322e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0263e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0143e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0097e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0047e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0001e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.9578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.9163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.6671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.6280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.5903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.5530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.5171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.4829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.4497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.4161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.3828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.3502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.3182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.2862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.2544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.2232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.6919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.4941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.4749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.4559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.4370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.4193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.3315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.3144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.2976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.2811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.2644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.2477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.2312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.2147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.1654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.1492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.1331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.1172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.1015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.0859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.0240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.0087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.7904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.7765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.1163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2910 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 197/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.6198e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.5975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.6253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.9973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.9745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.9635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.9551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.9468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9309e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.9157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.9054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.9032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.9010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.6184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2975 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 198/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.2548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.1441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.1436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.0276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.9795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.9406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.9060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.7925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.8019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.8211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.8494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.8519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.8539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.8556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.8568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.8577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.8585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.8595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.8608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.8621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.8633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.8645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.8652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.8658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.8658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.8653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.8648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.8644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.8639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.8633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.8628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.8624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.8617e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.8607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.8599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.8590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.8579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.8567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.8564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.8560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.8555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.8550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.8545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.8539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.8533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.8526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.8519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.8511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.8503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.8493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.8483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.8473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.8463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.8453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.8442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.8432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.8421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.8409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.8357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.8343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.8330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.8317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.8304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.8291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.8278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.8117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.8104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.8090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.8076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.8061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.8047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.8032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.8017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.8002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.7986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.7971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.6193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3030 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 199/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.2531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.3112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.3380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.2954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.2751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.2553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.3392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.5166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.5770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.6263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.6626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.6890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7649e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4800e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.1457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3091 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 200/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.1828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.9320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.7928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.6588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.5432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.4521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.3803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.3163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.2596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.1805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.1215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.0630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.0454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.0305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.0049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.0567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3143 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 201/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.0502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.9836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.3924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.2236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.2229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.2222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.2151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.2026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3157 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 202/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3580e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3797e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3834e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.3855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3175 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 203/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.5470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.0776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.5159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.4104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2309e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2100e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1800e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3226 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 204/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0405e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.0212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.9937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.9850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.9765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.9683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.9595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.9502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.9411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.9238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.9152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.9067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.8987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.8907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.8832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.8759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.8690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.8472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.8400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.8324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.8246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.8171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.8023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.7952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.7884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.7819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.7753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.7685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.7617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.7553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.7692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.7825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.7951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.8192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.8306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.8419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.8530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.8635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.8734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.8829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.8920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.9006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.9089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.9169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.9249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.9325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.9400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.0314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.9659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3287 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 205/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.7590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.7951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.4272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.8191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.0694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.2214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.3069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.3482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.3593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.3505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.3252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.2884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.2440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.1944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.1408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.0845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.0270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.9694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.9117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.7466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.6933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.5893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.5420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.4954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.4554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.4157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.3766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.2635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.2273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.1915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.1564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.1219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.0881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.0548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.0221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.9901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.9589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.9284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.8986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.8696e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.8413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.8134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.7071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.6084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.5850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.5620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.5395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.5174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.4956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.4743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.4533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.4326e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.4123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.3350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.3165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.2985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0456e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9782e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.9653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.9525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.9400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.9276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.9153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.9033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.8913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.8564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.8227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.8118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.8011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.7904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.7799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.7695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3285 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 206/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.0197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.9145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.6812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.5071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.3482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.2125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.9897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.9450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.9123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.8876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.8757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.8575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.8352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.8232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.8068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.7864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.7716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.7608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.7493e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.7037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.7287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.7328e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.7432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.7417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.7404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.7212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.7169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.7136e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.5893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.5840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.5616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.5302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.4984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.4952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.4919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.4889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.4859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.4830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.4798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.4766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.0155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3317 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 207/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.6913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.9904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.7774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.6380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.5349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.4584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.3665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.0920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.0665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.0440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.9986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.9838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.9729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.0271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.0633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.1317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.2213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.5303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.5328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.5348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.5455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.5599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.5875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.5914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.5952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.5988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.6025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.6058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.6092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.6123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.6153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.6185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.6215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.6244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.6272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.6299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.9487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3307 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 208/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 6.9976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 7.2306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 1.0000 - loss: 7.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 7.3761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 7.2852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 7.1790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 7.1140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.0468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 6.9950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 6.9527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 6.9283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 6.8984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 6.8780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 6.8611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 6.8516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 6.8352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 6.8165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 6.8012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 6.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - 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6.7330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 6.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.7286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.7243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.7202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.7152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.7104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.7066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.7036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 6.7020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 6.7010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 6.7004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 6.7004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 6.6994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 6.6977e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.6389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.6363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 6.6339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.6312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.6284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 6.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.6228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.6198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.6190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.6183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.6176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.6167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.6158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.6114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.6125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.6136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.6145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.6168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.6191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.6214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.6233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.6250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.6264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.6278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.6289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.6299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.6308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.6316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.6323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.6332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.6341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.6350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.6357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.6364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.6369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.6373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.6381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 6.7071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3359 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 209/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.5378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.1758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.9363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.7046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.6565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.5856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.5629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.5330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.4876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.4455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.4256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.3994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.3914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.3833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.3737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.3556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.3471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.3390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.3343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.3261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.3200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.3130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.3076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.3003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.2917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.2831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.5384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.7696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.9778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.1667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.3398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.4965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.6410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.7736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.8958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.0070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.1087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.2024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.2887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.3673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.4394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.5069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.5699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.6282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.6827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 8.7334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 8.7804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 8.8233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 8.8624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 8.8987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 8.9333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 8.9647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 8.9937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.0209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.0461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.0692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.0908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.1114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.1309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.1484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.1642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.1789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.2046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.2157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.2260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.2520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.2594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.2730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.2785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.2832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.2905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.2932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.2954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.2975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.2992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.3004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 9.3015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 9.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 9.3030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 9.3029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 9.3024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 9.3015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 9.3003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.2987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.2967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.2946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.2894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.2866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.2839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.2808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.2776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.2742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.2707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.2671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.2630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.2588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.2546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.2500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.2363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.2314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.2263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.2106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.2052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.1997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.1942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.1884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 8.5076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3360 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 210/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 7.9251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 7.6187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 7.5243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.2745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 7.0326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.8932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.8016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.7270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.6926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.6574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.6309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.5952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.5732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.5570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.5411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.5204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.4973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.4733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.4506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.4260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 6.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.3739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 6.3581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 6.3497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 6.3418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 6.3357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 6.3273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.3173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.3070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 6.2974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.2869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.2754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.2638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.2536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 6.2437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 6.2341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 6.2257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 6.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 6.2080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 6.1985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 6.1889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 6.1798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 6.1711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 6.1621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 6.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 6.1458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 6.1377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 6.1299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 6.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 6.1157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 6.1082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 6.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 6.0926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 6.0852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 6.0785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 6.0717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 6.0650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 6.0587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 6.0526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 6.0466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 6.0409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 6.0353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 6.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 6.0237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 6.0180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 6.0122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 6.0065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 6.0011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.9956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.9903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.9846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.9793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.9746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.9700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.9651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.9602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.9551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.9500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.9450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.9398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.9348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.9300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.9252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.9205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.9160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.9115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.9068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.9021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.8974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.8927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.8880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.8831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.8737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.8691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.8648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.8605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.8356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.8313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.8271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.8231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.8190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.8150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.8112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.8076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.8006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.7971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.7935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.7900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 5.3716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3400 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 211/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.8681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.8667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.7668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.6625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.5738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.4933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.4175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.3572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.3197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.2800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.2757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.2710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.2653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.2532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.2281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.2206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.2196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.2171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.2133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.2101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.2045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.2064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.2085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.2099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.2088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.2060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.2048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.2027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.2002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.1921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.1878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.1829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.1786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.1747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.1712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.1665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.1615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.1564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.1510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.1114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.1064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.1010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.0955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.0893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.0821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.0784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.0749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.0727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.0705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.0685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.0579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.0528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.0501e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.6217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3428 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 212/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.1198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.4944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.6106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.5575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.5265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.3202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.3096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.3056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.3012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.2908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.2861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.2811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.2613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.2874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.2882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.2897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.2913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.2948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.2977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.3006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.3032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.3056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.3103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.5477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.9175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.9574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.9959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.3192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.3460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.3820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.6896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.5703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3411 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 213/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 5.7199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.7675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.6203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.4900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.4016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.3275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.2696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.2198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.1818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.1239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.1173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.1108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.1057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.0964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.0934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.0827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.0425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.0383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.0342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.0299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.0400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.0485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.0560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.0631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.0693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.0752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.0858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.0901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.1019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.1126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.1222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.5230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.6047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.9397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.2893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.6241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.9444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.5457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.0164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.3689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.4236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.8002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.7239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0034e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0857e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2784e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3822e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4908e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7428e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8814e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0332e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1923e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5576e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7622e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9794e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4577e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.7044e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.9608e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2263e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4989e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.7787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.0683e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.3710e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6887e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.0173e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9999 - loss: 4.5114e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9765 - val_loss: 0.2762 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4984 Epoch 214/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9988 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9988 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9991 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993 - val_accuracy: 0.9761 - val_loss: 0.2860 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4985 Epoch 215/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - 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9.7586e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9997 - loss: 9.7038e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9997 - loss: 9.6455e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9997 - loss: 9.5825e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9997 - loss: 9.5161e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9997 - loss: 9.4498e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9997 - loss: 9.3841e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9997 - loss: 9.3192e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 7.5351e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 7.4818e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 7.4292e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 7.3773e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 7.3260e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 7.2754e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 7.2254e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 7.1760e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9998 - loss: 7.1273e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9998 - loss: 7.0792e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9998 - loss: 7.0318e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9998 - loss: 6.9849e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9998 - loss: 6.9387e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9998 - loss: 6.8931e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9998 - loss: 6.8481e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9998 - loss: 6.8037e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9998 - loss: 6.7599e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9998 - loss: 6.7166e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9998 - loss: 6.6740e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9998 - loss: 6.6318e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9998 - loss: 6.5902e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9998 - loss: 6.5492e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9998 - loss: 6.5087e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9998 - loss: 6.4687e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9998 - loss: 6.4292e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9998 - loss: 6.3902e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9998 - loss: 6.3517e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9998 - loss: 6.3137e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9998 - loss: 6.2762e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9998 - loss: 6.2392e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9998 - loss: 6.2026e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9998 - loss: 6.1665e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9998 - loss: 6.1308e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9998 - loss: 6.0956e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9998 - loss: 6.0608e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9998 - loss: 6.0265e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9998 - loss: 5.9925e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9998 - loss: 5.9590e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9998 - loss: 5.9259e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9998 - loss: 5.8932e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 5.8608e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 5.8289e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 5.7974e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 5.7662e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9998 - loss: 5.7354e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9998 - loss: 5.7049e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9998 - loss: 5.6748e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9998 - loss: 5.6450e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9998 - loss: 5.6156e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9998 - loss: 5.5866e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 5.5578e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 5.5294e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 5.5013e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 5.4735e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 5.4461e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 5.4189e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 5.3920e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 5.3655e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 5.3392e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 5.3132e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 5.2875e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9999 - loss: 2.2280e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9771 - val_loss: 0.2811 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 216/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.6939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.6842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.7388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.5661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.2611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.1605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.0730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.9887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.9217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.8323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.8557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.8671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.8735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.8690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.8626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.8643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.8657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.8670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.8672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.8678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.8679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.8680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.8663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.8636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.8607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.8576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.8534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.8483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.8430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.8380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.8325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.8271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.8222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.8175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.8120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.8062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.8000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.7937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.7870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.7799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.7731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.7663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.7592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.7522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.7454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.7387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.7318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.7248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.7175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.7103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.7032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.6958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.6884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.6812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.6738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.6665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.6592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.6521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.6447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.6372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.6296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.6221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.6146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.6069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.5992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.5620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.5546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.5471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.5169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.4942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.3988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.3916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.3845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.3774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.3703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.3632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.3561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.3490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.3418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.3347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.3277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.3206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.4847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2901 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 217/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.6754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.6412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.5925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.5126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2198e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.1025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.1013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 2.9618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2962 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 218/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.5795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.6570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.7088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.6804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.5919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.5585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.5302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.5000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.4419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.4388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.4366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.4337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.4323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.4293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.4260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.4234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.4199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.4168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.4171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.4179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.4174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.4166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.4151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.4133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.4110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.4080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.4049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.4025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.3999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.3975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.3953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.3933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.3921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.3906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.3888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.3869e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.3941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.4003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.4059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.4112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.4159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.4203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.4244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.4284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.4318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4612e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.4637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.4661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.4683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.4704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.4723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.4740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.4812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.4822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.4832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4839e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.4780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.4772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.4764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.4756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.4747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.4739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.4695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.4686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.4677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.3572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3032 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 219/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.6284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.4199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.2451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.1689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.9922e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.9643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.9471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.9053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.8999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.8943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.8722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.8677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.8630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.8588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.8552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.8528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.8500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.8471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.8442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.8418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.8394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.8369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.8344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.8324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.8305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.8288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.8273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.8262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.8249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.8235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.8223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.8210e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.8195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.8179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.8069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.8057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.8045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.8035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.8022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.8010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.7907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.7946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.7984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.8019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.8052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.8083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.8112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.8140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.8165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.8189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.8212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.8233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.8254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.8273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.8293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.8311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.8327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.8343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.8357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.8371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.8383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.8394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.8405e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.8415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.8425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.8435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.8444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.8453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.8460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.8468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.8474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.8481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.8486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.8490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.8495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.8499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.8505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.8510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.8515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.8519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.8523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.8526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.8528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.8531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.8535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.8608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3075 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 220/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.7977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.8012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.8151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6883e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5130e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4414e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4405e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.3611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3118 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 221/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2170e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2170e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3132 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 222/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1838e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1326e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3167 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 223/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.0071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.0060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 9.9535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 9.8116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 9.7013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 9.6228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 9.7956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 9.8670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 9.9069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.9488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.9654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.9811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.0009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.9392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.9238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.9099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.8986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.8925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.8817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.8699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.8563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.8426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.8280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.8110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.7941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.7806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 9.7684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 9.7573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 9.7490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 9.7411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 9.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 9.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.6998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.6887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.6770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.6663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.6562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.6467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.6377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.6294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.6229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.6152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.6069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.5987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.5906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.5825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.5738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.5536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.5514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.5492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.5364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.5329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.5288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.5246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.5207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.5165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.5099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.5068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.5032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.4993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.4952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.4910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.4869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.4824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.4779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.4736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.4652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.4618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.4321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.4288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.4256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.4226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.4200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.4177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.3973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.3942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.3909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.3714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.3679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.3643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.3605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.3567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.3529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.3490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.8845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3201 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 224/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.4101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.2305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.3795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.1041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.9754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2414e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1897e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.4931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3229 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 225/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.5186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.5704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.5999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.4174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.2114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.0583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 7.9778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.9119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.8322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.7844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.7534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.7407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.7333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.7260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.7206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.6931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.6783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.6611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.6411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.6226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.6090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.5936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.5794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.5682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.5240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.4761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.2989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.2946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.6917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3260 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 226/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.0053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.1549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.9138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 7.7901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.5835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.8295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.9246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.9351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.9343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.9294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.9132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.8872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.8533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.8209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.7868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.7493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.7130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.5813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.5255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.4695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.4440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.2003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.1799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.0969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.0772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.0582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.0406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.0237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.0076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.9918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.9126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.8973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.8827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.8680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.8537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.8471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.8337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.8265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.8189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.8112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.8033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.7951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.7869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.7791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.7710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.7629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.7552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.7478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.7401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.7114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.7039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.6892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.6819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.6748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.6677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.6608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.6537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.6465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.5963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.7412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3268 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 227/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.1764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.3689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.3425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.2056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.8839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.7511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.6441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.5396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.4486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.3794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.3131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.2636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.2230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.1971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.1667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.1354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.0776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.0546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.0304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.0085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.9926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.9798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.9675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.9570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.9521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - 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5.8880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.8814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.8754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.8705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.8665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.8614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.8557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.8503e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.7117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.7099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.7081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.7064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.7047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.7030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.7015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.6999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.6989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.6977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.6951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.6937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.6924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.6910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.6897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.6882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.6869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.4692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3308 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 228/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 7.3895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.7597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.5933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.2179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.1600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.9786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.9420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.9051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.7919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.7144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.7009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.6864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.6728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.6593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.6457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.6311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.6172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.6042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.5913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.5794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.5681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.5460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.5345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.5231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.5123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.3143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.3101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.3065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.1987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.1951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.1915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.1882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.9576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3328 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 229/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.4191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.4764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.5843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.4098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.3063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.2446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.2198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.1819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.0809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.0636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.0504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.0325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.0146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8056e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.8448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.8459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.8469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.8479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.8486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.8495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.8503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.8510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.8514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.8519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.8524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.8532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.8536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.8541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.8545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.8545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3353 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 230/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.7050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.0186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.2363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.0076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.9313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.8029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.7187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.6840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.6556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.6354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.6247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.6084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.5917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.5752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.4194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.4115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.4032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.3952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.3810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.3746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.3691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.3570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.3486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.3441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.3394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.3352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.3316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.3275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.3238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.3206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.3176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.2973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.2903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.3011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.3047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.3080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.3111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.3168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.3197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.3227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.3254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.3278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.3301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.3323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.3407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.3422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.3437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.3452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.3468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.3483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.3542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.3551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.3566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.3575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.3583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.3608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.3611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.3612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.3608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.3605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.3601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.3208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3377 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 231/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.4183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.2396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.2095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.9055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.8012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.7396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.6697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.5954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.5331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.4876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.4475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.4132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.3883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.3710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.8823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.3007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.6439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.3710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.5418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.6870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.8113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.9166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.0099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.1554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.2096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.3257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.4328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.4316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.4081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.3993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.3792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.3688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.3584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.3471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.3355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.3232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.2981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.2849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.2713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.2579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.2165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.2033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.1898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.1760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.1621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.1480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.1339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.1197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.8952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.8832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.8713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.8595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.8477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.8361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.8246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.8133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.8020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.7907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.7796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.7685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.7576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.7032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.6924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.6816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.5990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.5892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.5795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.3899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3369 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 232/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.3488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.2576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.9123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.7742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.6700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.5826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.1819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.1610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.1410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3403 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 233/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.6692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.6097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.5474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.4368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.5063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.5757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.1038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.1199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.1346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.1485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.2068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.2165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.2253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.2553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.2618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.2743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.2800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.2852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.2899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.2944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.2982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.3250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.0325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3422 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 234/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.3163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.2885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.2531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.2257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.1922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3442 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 235/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.9007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.8366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.8311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.8291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.8280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.8265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.8070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.8068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.8060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.8049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.7984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.7964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.7950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.7938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.7926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.7917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.7909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.7898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.7887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.7892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.7899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.7899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.7899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.7901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.7907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.7906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.7904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.7901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.7894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.7889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.7832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.8019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3461 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 236/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.7925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.8752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.9502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.9769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.9821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.9864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.9900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.9932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.9971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.0002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.0028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.0053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.0075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.0096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.0114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.0130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.0147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.0194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.0191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.0188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.6657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3467 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 237/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 3.9450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.6426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.5054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.3931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.3044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.2384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.1865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.1332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.0892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.0322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.0120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.9762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.9656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.9572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.9527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.9473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.9406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.9356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.9329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.9278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.9269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.9248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.9224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.9196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.9167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.9148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.9123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.9102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.9983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.0787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.1528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.2843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.4457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.4918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.6086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.6422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.6731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.7796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.8016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.8220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.8589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.8751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.8903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.9046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.9178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.9301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.9419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.9531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.9634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.9729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.9816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.9972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.0040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.0102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.0161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.0216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.0266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.0313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.0357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.0397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.0433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.0467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.0529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.0556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.0580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.0661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.0679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.0696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.0710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.0721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.0731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.0737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.0741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.0764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.0772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.0759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.0710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.0697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.0668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.0653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.0637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.0621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.8664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3474 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 238/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.8235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.6018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.6036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.6040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.6074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.6087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.6101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.6111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.6121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.6137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.6218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.6236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.6252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.6268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.6285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.6303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.6455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.6463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.6470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.6477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.6490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.6494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.6509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.6511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.6512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.6513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.6514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.6515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.6516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.6516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.6515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.6514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.6512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.6509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.6506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.6503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.6499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.6495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.6493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.6490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.6490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.6489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.6488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.6486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.6481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.6478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.6475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.6472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 2.6080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3496 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 239/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.1256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.5779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.2534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.9584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.7324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.5646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.4402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.3321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.1581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.0339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.7998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.7756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.7520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.7121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.6954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.6797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.6661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.6545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.6429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.6313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.6200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.6091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.5876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.5788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.5713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.5638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.5570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.5509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.5391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.5331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.5271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.5215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.5162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.5106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.5052e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.4689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.4649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.4228e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3908e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.3645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3525 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 240/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.5810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.5962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.3412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.3408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.3406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.3402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.3400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.3397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.1629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3543 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 241/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.2760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.1749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 2.1152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 293ms/step - accuracy: 1.0000 - loss: 2.0969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 293ms/step - accuracy: 1.0000 - loss: 2.0729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.0559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.0451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.0336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.0273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.0217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.0850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.0880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - 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2.0880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.0851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.0839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.0829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.0825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.0818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.9028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3565 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 242/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.3847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.9994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.9891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.9621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.9546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.9273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.9219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.9166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.5529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.0412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.3057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.5364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.6410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.8328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.9280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.0226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.1136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.1991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.3544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.4247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.4904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.5521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.6113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.6671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.7191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.7745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.8758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.9222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.9658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.1155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.1474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.1777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.2062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.2337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.2598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.3230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 5.3598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 5.3949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 5.4280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 5.4594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 5.4893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 5.5210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 5.5512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 5.8968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 6.2295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.8586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 7.3625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 7.8488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 8.3186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 8.8931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 9.4488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.0024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.0606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.1171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.1755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.2384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.3013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.3656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.5158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.6634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.8173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.9837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 2.1577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 2.4031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 2.7186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 3.0425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 3.3840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 3.7747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 295ms/step - accuracy: 1.0000 - loss: 4.2264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 295ms/step - accuracy: 1.0000 - loss: 4.7434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 295ms/step - accuracy: 1.0000 - loss: 5.3474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 295ms/step - accuracy: 1.0000 - loss: 6.0026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 295ms/step - accuracy: 1.0000 - loss: 6.7501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 295ms/step - accuracy: 1.0000 - loss: 7.5721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 295ms/step - accuracy: 1.0000 - loss: 8.4852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 9.5005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 1.0592e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 1.1830e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 1.3208e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 1.4795e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 1.6537e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 1.8483e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 299ms/step - accuracy: 0.9999 - loss: 2.5005e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9749 - val_loss: 0.3251 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 243/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9987 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 0.9986 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9986 - loss: 0.0042 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9986 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9986 - loss: 0.0041 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9987 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9987 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9987 - loss: 0.0040 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9987 - loss: 0.0039 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9987 - loss: 0.0038 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4990  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9987 - loss: 0.0037 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9987 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9988 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9988 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9988 - loss: 0.0036 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9988 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9988 - loss: 0.0034 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9988 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0033 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4991 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4992 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994 - val_accuracy: 0.9748 - val_loss: 0.2422 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4977 Epoch 244/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 6.6801e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 - val_accuracy: 0.9767 - val_loss: 0.2773 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 245/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.2655e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2424e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2283e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1841e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1463e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1161e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0934e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0725e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0529e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0377e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0283e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0185e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0101e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0027e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.9614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.8924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.8260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.7622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.7009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.6397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.5791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.5249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.4781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.4307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.3119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.2727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.2333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.8886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.7737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.6894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.5892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.5658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.5423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.5188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.4266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.4040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.0937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0456e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.9973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.9817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.9661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.9508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.9357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.9208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.9058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.8908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.7876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.7733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.7592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.7453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.7313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.7173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.7033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.6894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.6755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.6616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.6478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.6343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.6209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.6076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4904e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.9578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.2813 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 246/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.3547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.4149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.4507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.3494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.2679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.2077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.0417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.0385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.0349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.0314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.0275e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.0231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.0191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.0156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.0118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.0084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.0051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.0019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.9004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.8968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.8932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8574e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7725e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.3558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2890 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 247/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.4268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5969e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5869e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5720e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5318e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4883e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2947 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 248/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.4080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.1347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.1182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.1032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0037e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9455e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.2995 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 249/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.0375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.5107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.5520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.5624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9839e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9100e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3030 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 250/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.6860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 1.0000 - loss: 1.6551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 1.0000 - loss: 1.6509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 1.6057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 1.5720e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 1.5478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 1.5323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.5153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.4989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.4870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.4818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.4757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.4724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.4699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 1.4682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 1.4643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 1.4598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 1.4554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 1.4512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 1.4469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 1.4419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 1.4373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 1.4339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 1.4303e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.4112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.4091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.4070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.4051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.4031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.4014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.3998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.3986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.3970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.3954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.3937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.3921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.3905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.3889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.3874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.3861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.3848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.3802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.3789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.3777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.3766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.3722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.3716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.3711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.3706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.3702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.3698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.3693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.3688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.3683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.3677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.3670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.3663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.3657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.3651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.3646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.3644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.3642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.3639e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.3636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.3632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.3629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.3625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.3620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.3615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.3611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.3607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.3602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.3598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.3594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.3590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 1.3586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.3581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.3577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 1.3573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.3569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.3564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.3561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 1.3558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.3555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.3552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 295ms/step - accuracy: 1.0000 - loss: 1.3550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.3548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.3546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.3546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 1.3545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.3544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.3542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.3541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.3540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.3539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3538e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.3534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.3532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.3530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.3256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3067 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 251/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.5053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.4623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.4223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.3864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.3619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.3394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.3180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.3030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.2920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.2823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.2759e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.2704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.2666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.2798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.2773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.2763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.2754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.2743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.2733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.2724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.2714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.2704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.2692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.2680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.2668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.2656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2456e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2437e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2414e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2405e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.2086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3098 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 252/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0696e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1868e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3132 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 253/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.7729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 9.9193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.9610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.8855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.8271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.7790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.7010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.6314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.5038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.4226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.3871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.4008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.4022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.4010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.3994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.3983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.3852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.3825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.3814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.3790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.3762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.3730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.3689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.3636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.3571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.3513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.3474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.3436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.3406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 9.3377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 9.3350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 9.3317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.3278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.3236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.3204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.3077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.3045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.3007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.2971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.2935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.2902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.2862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.2819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.2774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.2731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.2685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.2641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.2595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.2510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.2289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.2251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.2209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.2175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.2016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.1991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.1964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.1936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.1909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.1880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.1848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.1815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.1785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.1753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.1723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.1694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.1666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.1467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.1430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.1396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.1361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.1326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.1123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.1091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.1060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 8.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3145 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 254/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.5864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.4413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.3193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 8.8964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 8.7674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.6479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.5552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.5062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.4500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.4035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.3677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.3409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.3071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.2771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.1963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.1740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 8.1399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 8.1233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 8.1122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 8.1079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 8.1041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 8.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.0915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.0836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.0767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.0682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.0487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.0405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.0316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.0239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.9959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.9882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.9806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.9742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.9712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.9677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.9652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.9622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.9596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.9574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.9560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.9536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.9516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.9491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.9463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.9435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.9373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 7.9352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.9326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.9307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 7.9289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.9275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.9255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 7.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.9209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.9184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.9157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 7.9128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 7.9096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 7.9066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 7.9035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 7.9005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 7.8975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 7.8950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 7.8920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 7.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 7.8861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 7.8832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 7.8800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 7.8767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 7.8734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.8704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.8674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.8646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.8619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.8594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.8567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.8537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.8378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.8354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.8330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.8323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.8317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.8312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.8303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.8293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.8283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.8274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.8266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.8255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.8234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.8223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.8205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.8187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.8192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.8198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.8200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.8199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.8198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.8198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.8059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3176 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 255/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.4320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.0666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.8585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.5492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.2949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.1594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.9845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.9027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.7721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.7097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.6902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.6628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.6371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.6183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.5997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.5812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.5599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.5568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.5567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.5555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.5559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.5559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.5572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.5558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.5523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.5323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.5254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.5213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.5159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.5106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.5054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.5008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.4558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.4419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.7389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3201 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 256/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 6.5142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.9023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.0502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.8143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.7248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.6489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.5057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.4297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.4184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.4176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.4148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.4112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.3996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.3951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.3931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.3908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.3885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.3860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.3856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.3850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.3842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.3835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.3820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.3812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.3804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.3796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.3738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.3723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.3708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3227 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 257/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.7218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.2458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.9057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.6219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.4072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.1128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.9958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.9382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.8766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.8264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.7828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.6653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.6278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.5909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.5532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.5182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.4557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.4297e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.9026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.9007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.8941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.8926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.8912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.8898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.6086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3246 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 258/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.3440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.0364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.8922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.7068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.5925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.4982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.7235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.8499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.9088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.9477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.9881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.0201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.0286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.0335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.0271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.0184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.9833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.9669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.9502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.9439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.9345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.9262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.9177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.9098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.8996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.8887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.8768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.8667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.8552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.8302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.8198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.8094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.8000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.7910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.7832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.7755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.7677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.7612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.7802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.8008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.8190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.8353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.8505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.8641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.8769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.8887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.8995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.9089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.9169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.9241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.9304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.9515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.9546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.9580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.9612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.9635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.9652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.9664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.9674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.9678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.9677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.9674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.9672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.9666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.9672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.9682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.9681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.9680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.9675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.9670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.9661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.9650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.9638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.9569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.9552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.9534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.9516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.9498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.9506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.9512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.9516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.9520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.9521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.9521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.9522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.9523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.9514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.9508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.9501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.9496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.9489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.9482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.9447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.9436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.9423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.9410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.9396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.9381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.9366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.9351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.7569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3269 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 259/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.2280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.2241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.2864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.1840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.0829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.9524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.7819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.7546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.7246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.7178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.7057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.6910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.6782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.6659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.6515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.6354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.6216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.6123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.6031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.5630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.5662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.5677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.5723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.5741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.5753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.5765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.5776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.5784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.5787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.5787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.5794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.5799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.5806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.5869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.5878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.5882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.5874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.5871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.5868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.5865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.5861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.5850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.5847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.5843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.5841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.5836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.5832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.5827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.5823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.5817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.5809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.5803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.5797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.5778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.5730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.5725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.5720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.5715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.5712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.5709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.5665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.5658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.4815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3291 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 260/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.0406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.8278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.7928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.6430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.5571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.4820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.4232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.3734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.3341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.3131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.3066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.3053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.3090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.3058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.3013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.2951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.2814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.2726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.2645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.2604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.2558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.2534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.2515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.2509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.2488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.2586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.2621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.2655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.2672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.2686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.2701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.2709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.2719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.2729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.2742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.2752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.2763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.2770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.2773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.2774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.2768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.2760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.2758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.2751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.2745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.2740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.2739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.2732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.2724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.2714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.2708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.2700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.2660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.2654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.2649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.2135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.1871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3318 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 261/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.1154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.1240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.2380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.1835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.1261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.9782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.9255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.8816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.8558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.8295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.8194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.8109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.8088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.8016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.7942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.7875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.9109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.9271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.9422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.9545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.9724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.9884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.0019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.0138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.0262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.0372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.0486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.0707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.0871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.0943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.1061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.1367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.1407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.1442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.1496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.1521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.1559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.1578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.1596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.1614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.1631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.1647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.1673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.3321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.3686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.5000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.5362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.5706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.6034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.6348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.6650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.6940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.7217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.7481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.7736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.7980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.8441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.8871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.0123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.0277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.0426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.0569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.0708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.2012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.2181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.2260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.2335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.2476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.2541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.2605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.9962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3323 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 262/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.3586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.6931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.6031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.5062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.3582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.2993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.1937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.1610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.1314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.1107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.0963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.0878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.0677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.0579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.0326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.0194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.0087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.9988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.9917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.9859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.9815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.9756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.9711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.9663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.9616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.9563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.9507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.9457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.9414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.9368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.9327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.9293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.9265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.9228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.9152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.9115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.9079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.9041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.9002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.8970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.8935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.8903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.8875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.8849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.8821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.8792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.8736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.8709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.8679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.8653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.8630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.8605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.8583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.8547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.8529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.8488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.8467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.8446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.8423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 3.8400e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.8248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.8232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.8196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.8178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.8161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.8144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.8128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.8112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.8098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.8083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.8067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.8052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.8040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.8027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 295ms/step - accuracy: 1.0000 - loss: 3.8015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.8002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.7991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.7979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.7968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.7958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.7950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.7914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.7906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.7888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.7879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.7871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.7833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.7825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.7816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.7807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.7769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.6705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3344 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 263/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.3952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.1972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.1949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.0920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.0371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.0313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.2726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.4338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.4387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.4401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.4419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.4409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.4396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.4378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.3410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.3335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.3262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.3037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.3121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.3603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.4508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.4923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.6356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.6668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.6962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.8208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.8422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.8629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.9014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.9195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.9369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.9535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.9695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.9845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.9988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.0122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.0252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.0991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.4026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3345 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 264/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.3080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.3821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.1056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.9532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.3196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.3799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.5417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.5364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.4642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.4560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.4419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.4340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.4284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.3998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.3726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.3666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.3604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.2944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.3347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3359 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 265/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.2656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.2544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.1795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.1128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.9145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.8458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.8378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.8243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.7403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.7243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.7186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.7132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.7094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.7040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.6987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.6928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.6679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.6630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.6586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.6548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.6551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.1123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.3149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.3737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.4289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.4805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.6161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.6553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.6922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.9901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.0064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.0215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.1052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.1575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.1631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.1734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.1782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.1826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.1866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.2014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.2054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.2087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.2111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.2119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.2128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.2129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.2127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.2126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.2119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.0221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3387 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 266/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.5166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.5279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.4536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.3130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.1922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.0907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.0070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.7955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.7526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.7105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.6854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.6661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.4636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.4604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.4577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.4550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.4525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.4418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.4397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.4001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.4644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.5332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.5996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.6639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.7259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.7858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.8437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.8998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.0065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.1067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.2005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.3303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.3708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.4100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.4483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.4853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.5213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.5563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.5903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.6233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.6553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.6863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.7166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.7460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.7745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.8022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.8292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.8554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 7.9752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3399 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 267/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.1055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.0068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.9914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.9167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.8323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.6975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 3.6393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 3.5237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.5011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.4761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.4735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 3.4742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 3.4737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 3.4721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 3.4716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 3.4702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 3.4734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 3.4764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 3.4794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 3.4810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 1.0000 - loss: 3.4815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 1.0000 - loss: 3.4809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 294ms/step - accuracy: 1.0000 - loss: 3.4797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 3.4783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 3.4759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 3.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 294ms/step - accuracy: 1.0000 - loss: 3.4727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 1.0000 - loss: 3.4714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 1.0000 - loss: 3.4702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 294ms/step - accuracy: 1.0000 - loss: 3.4687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 1.0000 - loss: 3.4674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 1.0000 - loss: 3.4654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 294ms/step - accuracy: 1.0000 - loss: 3.4629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.4599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.4568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.4501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.4467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.4447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.4424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.4401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.4378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.4356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 3.4331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.4305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.4280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.4257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.4234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.4183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.4162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.4138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.4117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.4083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.4065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.4046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.4029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.4012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.3993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.3973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.2571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3405 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 268/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.6223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.4379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.1175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.1309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.1035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.0979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.0899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.0818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.0533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3421 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 269/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.8318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.6969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3447 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 270/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.2973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.4583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.4568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.4550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.4516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.4505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.4455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.4443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.4431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.4418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.4403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.4386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.4372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3473 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 271/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.5607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.6572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.6152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.5176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.3903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.3772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.3661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.0686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3492 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 272/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.9899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.4048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.3561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.3217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.2837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.2099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.1862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.1775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.1710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.0348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.0294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.0243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.9844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.9868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.9893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.9981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.0000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.0017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.0065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.0091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.0128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.0140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.0151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.0160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.0169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.0177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.0185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.0192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.0197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.0204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.0209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.0214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.0223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.0235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.0247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.0258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.1047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3504 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 273/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.6373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.4374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.8056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.7861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.7836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.7811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.7787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.7761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.7950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.7979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.8004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.8027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.8046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.8062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.8196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.8198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.8199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.8201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.8202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.8202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.8203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.8203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.8203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.8201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.8200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.8199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.8198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.7911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3527 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 274/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.7007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3550 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 275/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3562 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 276/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.5974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.5990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3588 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 277/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.5780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.5784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.5851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.5886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.5904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.5915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.5927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.5945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.5964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.5978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.6003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.6023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.6049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.6072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.6091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.6102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.6120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.6116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.6124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.6129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.6135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.6140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.6144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.6145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.6145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.6144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.6142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.6139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.6134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.6128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.6123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.6116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.6114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.6112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.6109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.6104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.6099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.6094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.6088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.6080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.6074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.6071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.6066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.6062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.6058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.6057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.6055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.6051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.6047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.6043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.6038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.6033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.6028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.6023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.6018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.6014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.6009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.6005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.6002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.5999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.5995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.5992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.5989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.5981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.5977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.5973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.5969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3573 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 278/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.4949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.4426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.4400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.4359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.4348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.4334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.4311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.4291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.4279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.4266e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8231e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.9287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.9291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.9294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.9296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.9294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.9292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.9289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.9286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.9282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.9278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.9249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.9243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.9237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.9230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.9223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.9215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.9208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.8315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3575 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 279/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.4635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.4667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.3762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.3704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.3673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.3634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.3508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.3425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.3410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.3402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.3393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.3385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.3379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.3349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.3341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.3332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.3324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.3318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.3311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.3306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.3302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.3299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.3295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.3290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.3285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.3281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.3275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.3269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.3262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.2703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3594 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 280/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.1688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.3408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3627 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 281/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.5828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.5361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.5400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.1083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.9442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.3960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.6374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.8229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.8453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 4.8388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.8181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.7998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.7854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.7725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.1094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.6376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.0908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.4741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.7992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.1061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.3715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.6151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.8295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.2111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.3763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.7672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.0440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.8132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.0474e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2222e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4247e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6490e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.9444e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2649e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6393e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0551e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4805e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.0289e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.6127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2692e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.9833e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.7752e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.6752e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.7122e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.8564e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1116e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2517e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3987e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.5442e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.6983e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9999 - loss: 1.8568e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9999 - loss: 2.0190e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 0.9999 - loss: 2.1840e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9999 - loss: 2.3503e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9999 - loss: 2.5227e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 0.9999 - loss: 2.6991e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9999 - loss: 2.8798e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9999 - loss: 3.0655e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9999 - loss: 3.2538e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 3.4415e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 3.6243e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 3.8056e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 3.9856e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9999 - loss: 4.1646e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9999 - loss: 4.3403e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9999 - loss: 4.5144e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9998 - loss: 4.6890e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9998 - loss: 4.8621e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9998 - loss: 5.0317e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9998 - loss: 5.2009e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9998 - loss: 5.3686e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9998 - loss: 5.5331e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9998 - loss: 5.6933e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9998 - loss: 5.8513e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9998 - loss: 6.0074e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9998 - loss: 6.1601e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9998 - loss: 6.3084e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9998 - loss: 6.4533e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9998 - loss: 6.5957e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9998 - loss: 6.7354e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9998 - loss: 6.8709e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9998 - loss: 7.0038e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9998 - loss: 7.1333e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9998 - loss: 7.2589e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9998 - loss: 7.3807e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9998 - loss: 7.4996e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.6152e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.7273e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.8358e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.9409e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9997 - loss: 8.0435e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9997 - loss: 8.1435e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9997 - loss: 8.2403e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9997 - loss: 8.3341e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9997 - loss: 8.4253e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9997 - loss: 8.5145e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9997 - loss: 8.6006e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9997 - loss: 8.6841e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9997 - loss: 8.7650e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9997 - loss: 8.8435e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9997 - loss: 8.9193e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9997 - loss: 8.9926e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9997 - loss: 9.0638e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9997 - loss: 9.1330e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9997 - loss: 9.2000e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9997 - loss: 9.2651e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9997 - loss: 9.3282e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9997 - loss: 9.3893e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9997 - loss: 9.4485e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9997 - loss: 9.5058e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9997 - loss: 9.5613e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9997 - loss: 9.6152e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9997 - loss: 9.6674e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9997 - loss: 9.7178e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9997 - loss: 9.7665e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9997 - loss: 9.8135e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9997 - loss: 9.8592e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996 - val_accuracy: 0.9767 - val_loss: 0.2939 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 282/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9996 - loss: 9.8758e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9997 - loss: 8.8887e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9997 - loss: 8.0584e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9997 - loss: 7.4300e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9997 - loss: 6.9645e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9998 - loss: 6.6732e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9998 - loss: 6.5931e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9998 - loss: 6.5695e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9998 - loss: 6.5172e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9998 - loss: 6.4742e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9998 - loss: 6.4500e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9998 - loss: 6.4179e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9998 - loss: 6.4125e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 6.4299e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 6.4377e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 6.4285e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9998 - loss: 6.4067e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9998 - loss: 6.3858e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9998 - loss: 6.3871e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.4039e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.4154e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.4220e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9998 - loss: 6.4233e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9998 - loss: 6.4272e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9998 - loss: 6.4363e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9998 - loss: 6.4507e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9998 - loss: 6.4674e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9998 - loss: 6.4804e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9998 - loss: 6.4894e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9998 - loss: 6.4924e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9998 - loss: 6.4918e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9998 - loss: 6.4894e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9998 - loss: 6.4867e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9998 - loss: 6.4910e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9998 - loss: 6.4999e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9998 - loss: 6.5068e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9998 - loss: 6.5115e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9998 - loss: 6.5133e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9998 - loss: 6.5138e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9998 - loss: 6.5136e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9998 - loss: 6.5157e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9998 - loss: 6.5193e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9998 - loss: 6.5238e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9998 - loss: 6.5284e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9998 - loss: 6.5306e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9998 - loss: 6.5317e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9998 - loss: 6.5321e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9998 - loss: 6.5346e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9998 - loss: 6.5387e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9998 - loss: 6.5436e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9998 - loss: 6.5473e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9998 - loss: 6.5488e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9998 - loss: 6.5488e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9998 - loss: 6.5479e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 6.5478e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 6.5497e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 6.5506e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 6.5508e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 6.5496e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 6.5477e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 6.5452e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 6.5431e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 6.5413e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 6.5386e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 6.5350e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 6.5303e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 6.5247e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 6.5184e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 6.5113e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 6.5043e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 6.4974e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 6.4902e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 6.4822e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 6.4737e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 6.4645e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 6.4547e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 6.4444e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 6.4336e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 6.4227e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 6.4116e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 6.4001e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 6.3885e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 6.3765e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9998 - loss: 6.3642e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9998 - loss: 6.3515e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9998 - loss: 6.3384e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9998 - loss: 6.3251e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9998 - loss: 6.3115e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9998 - loss: 6.2978e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 6.2838e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 6.2698e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 6.2555e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 6.2410e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 6.2263e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 6.2114e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 6.1964e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 6.1813e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 6.1660e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 6.1506e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 6.1350e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 6.1193e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 6.1036e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 6.0878e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 6.0719e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 6.0559e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 6.0398e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 6.0237e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 6.0076e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 5.9914e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.9751e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.9588e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.9425e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.9262e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 5.9099e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 5.8935e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 5.8771e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 5.8608e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 5.8444e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9998 - loss: 5.8280e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9998 - loss: 5.8117e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9999 - loss: 3.8656e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9768 - val_loss: 0.2901 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 283/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 8.0556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 8.4837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.7435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.6422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.4889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.2827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.3056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2456e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.1770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.1498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.1186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.9848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.9365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.8878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.8368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.7884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.7457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.7060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.6670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.6291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.5946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.5582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.5223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.2851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.2539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.2234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.1630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.1325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.0722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.0426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.0129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9007e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.1492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.1346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.1201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.1058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.0916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.0774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.0634e-06 - 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0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.9542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9147e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.9019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.8891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.8763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.8636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.8511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.8387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.8263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.8141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.8020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.7899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.7778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.7659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.7542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.7425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.7308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.7192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.7077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.3362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.3013 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 284/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.0082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7048e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.6775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.6690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.6579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.6122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.6056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4587e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4300e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4141e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.3791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3566e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4119e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4267e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.0560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.3126 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 285/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.7572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.9398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7405e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6758e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.4376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3189 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 286/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.7191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2100e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1800e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1735e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3240 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 287/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.1967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.1382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.8418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.0255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.0092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.9449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.9255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.9051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.8847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.8647e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.8449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.8255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.8062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.7235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.7096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.6959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.6827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.6697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.6334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.6221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.6114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.6009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.5908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.5811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.5717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.5625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.5535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.5449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.5365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.5284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.5205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.5128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.5055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.4847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.4782e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.4719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.4656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.4595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.4536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.4478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.4420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.4364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.4310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.4256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.4204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.4154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.4106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.4058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.4011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.3965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.3921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.3877e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.3833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.3829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.3825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.3820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.3815e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.3809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.3803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.3796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.3788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.3779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.3771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.3762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.3753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.3743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3734e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.3694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.3684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.3673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.3662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.3651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.3641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.3588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.3578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.3570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.3525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.3515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.3505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.3495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.3485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.3475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.3434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.3424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.3414e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.3403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.3360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.2078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.3260 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 288/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.0153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.8852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.7529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.6185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.5446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.4408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.3870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.3688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.3392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.3059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.2721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.2443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.2170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.1867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.1591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.1186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.0919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.0811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.0542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.0393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.0277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.0149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.0005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.9879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.9769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.9291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.9198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.9102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.9007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.8911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.8805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.8704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.8639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.8573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.8514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.8460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.8419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.8322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.8213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.8156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.8092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.8028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.7969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.7925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.7879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.7837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.7796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.7751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.7709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.7662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.7614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.7562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.7509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.7455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.7404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.7732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.8048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.8353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.8646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.8921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.9182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.9434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.9907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.0124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.0332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.0532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.0724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.0912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.1095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.1274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.1441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.1600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.1751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.1895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.2286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.2521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.2632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.2740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.2945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3246 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 289/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.0188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 8.2269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.5325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.5585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.7710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.8519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.3125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.5904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.7640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.9211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.8516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.8400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.8284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.7532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.7379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.7237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.6377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.6196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.6022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.5418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.5319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.5215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.5104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.4008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.3215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.2999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.2108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.1998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.1891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.1789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.1686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.1580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.1049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.9201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.9115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.9029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.8628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3296 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 290/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.3275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.3287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.1942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 7.0260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.8836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.7801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.6925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.6062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.5301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.4865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.4430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.3957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.3834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.3682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.3498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.3538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.3536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.3538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.3521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.3491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.3481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.3469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.3466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.3537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.3557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.3518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.3495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.3476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.4100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.4711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.5280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.5791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.6258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.6686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.7083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.7456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.8335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.8845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9750e-07 - 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0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.6985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.6989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.6991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.6990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.6987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.7055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.7118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.7178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.7236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.7290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.7341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.7484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.7563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.7598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.7630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.7659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.7688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.7716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.7741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.0729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3340 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 291/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.1747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.3623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.5211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.5415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.2630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.1897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.9932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.9775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.9597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.9430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.8100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.8027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.7951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.7866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.7781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.7731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.7677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.7631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.7599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.7575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.7541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.7501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.7460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.7416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.7370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.7258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.6989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.6946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.6900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.6852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.6804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.6753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.6704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.6660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.6058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.6028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.1488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3373 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 292/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.2894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.3097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.4971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.6553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.6704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.6583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.6312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.6131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.5899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.5112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.7697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.2396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.4287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.7322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.9641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.0585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.1398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.3252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.3724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.5391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.5620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.6087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.6183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.6251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.6296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.6326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.6351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.6349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.6197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.6146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.6090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.6024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.5955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.5885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.5810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.5729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.5658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.5584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.5504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.5241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.5056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.4560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.4460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.4357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.4146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.4039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.3213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.3114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.3013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.9995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3392 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 293/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.5261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.5870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.6713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.5401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.4050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.2863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.0737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.9824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.9428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.9119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.7907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.7712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.7560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.7459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.7140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.7092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.7028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.6960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.6894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.6773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.6711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.6657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.6608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.6564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.6519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.6480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.6381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.6328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.6281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.6234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.6182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.6133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.6089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.6044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.6001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.5833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.5787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.5739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.5537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.5352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.5309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.5267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.5225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.5183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.5146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.5108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.5071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.5035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.3985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.3964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.3944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.3865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.3844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.3825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.3805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.3784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.3763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.3744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.3724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.1445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3428 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 294/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.9989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.0587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.1654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.1182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.0411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.9696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.8873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.8533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.8216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.8061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.7720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.7659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.7572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.7512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.7369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.6987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.6943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.6913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.6891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.6858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.6817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.6739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.6705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.6586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.6575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.6569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.6558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.6549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.6537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.6455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.6440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.6426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.6417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.6413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6379e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.6234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.6069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.6051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.6043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.6034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.6026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.6018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.6009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.6001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.5001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3455 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 295/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.2312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.2588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.1962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.1403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.1090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.0943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.0976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.0982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.0975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.1074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.1154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.2373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.2385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.2428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.2627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.2656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.2706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.2728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.2747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.2765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3474 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 296/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2240e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6764e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.3726e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.1742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.0329e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 9.2661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 8.4322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.7576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.1986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.7273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.3248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.9750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.6689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.3983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.1570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.9398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.7432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.2509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.1126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.9846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.8660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.7557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.6527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.3812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.2255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.0866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.0224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.9615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.9036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.7959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.7457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6978e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.4859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.4484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.4123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7291e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5579e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4893e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4705e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.5106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.3475 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 297/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.6861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.9411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.4195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.9880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.6337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.3289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.0723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.2485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.1335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.0264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.9271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.8348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.7504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.6701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.1263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.0873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.0496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.0144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.7955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.6516e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.2298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.2221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.2146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.2094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.2040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.1987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.1933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.1880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.1666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.1615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.1516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.1317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.1268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.1219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.1170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.1123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.1076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.1030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.0851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.0807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.0763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.0720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.0547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.5497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3471 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 298/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.8305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 3.0138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.1140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.1196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.0957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.9735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.9334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.9329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.8449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3498 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 299/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.5965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.4307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.3577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.1063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.0236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.9568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.9045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6700e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3526 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 300/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.4030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.4209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.2265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3552 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 301/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.2024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.2236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.2215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.1936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3559 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 302/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.1498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.2468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.1794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.0952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.0062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.0048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.0040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.9416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.9408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.8573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3592 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 303/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.8621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.7430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.7425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.7420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.7346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.7339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.7331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3606 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 304/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.3352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.3295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.3234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.3174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.2882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.2826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.2769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.2715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.2662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.2608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.2499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.2446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.2397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.2296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.2246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.2198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.2105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.1536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.1496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.1455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.1376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.0263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.0238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.0214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.0189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.0165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.0140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.7231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3624 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 305/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 1.6860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.6775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.7742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.7628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.7284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.7095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.6854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.6618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.6387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.6166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.6007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.5883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.5891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.5919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.5938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.5924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.7013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.7920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.8684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.9316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.9858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.1301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.2493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.2561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.2619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.2776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.8073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.8282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.8479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.8665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.8841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.0046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.5949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.8966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.5716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.2360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.2135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.1654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.1506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.8467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6839e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.5736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.6107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1063e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2608e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4310e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9999 - loss: 2.1674e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9759 - val_loss: 0.3139 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 306/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9990 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9989 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9989 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9990 - loss: 0.0031 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9990 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9990 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 0.9990 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9991 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9991 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4993  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9991 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9991 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9992 - loss: 0.0025 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9992 - loss: 0.0024 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9992 - loss: 0.0023 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9992 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9993 - loss: 0.0022 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4994  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9993 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9994 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9994 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9997 - loss: 8.6241e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 - val_accuracy: 0.9768 - val_loss: 0.2865 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 307/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2168e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.8718e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.1786e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.4456e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6144e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.9256e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.1579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.2763e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.3455e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.3652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.3579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.3551e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.3358e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3102e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2773e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2640e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.2416e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.2291e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.2202e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.2040e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1830e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1587e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1327e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.1047e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0755e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0453e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0143e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9826e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9177e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8848e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8520e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.8191e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.7863e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7538e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7215e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6896e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6583e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5968e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5665e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5368e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5075e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4785e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4499e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4218e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3942e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3671e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3404e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.3143e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2886e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2634e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2385e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2139e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1898e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1660e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1426e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1195e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0969e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0746e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0527e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0312e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9892e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9686e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.9485e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.9286e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.9090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.8897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.8707e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.8520e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.8336e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.8155e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.7977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.7802e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.7630e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.7460e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.7292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.7127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6965e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6804e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6646e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.6337e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.6186e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.6038e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.5891e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.5746e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.5603e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5462e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5323e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5186e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5051e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4917e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4785e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4655e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4526e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4400e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4154e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4035e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3801e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3687e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3573e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3461e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3350e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3241e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3133e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3026e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2920e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2816e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2713e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2610e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2509e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2410e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2311e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2213e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2116e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0642e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3099 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 308/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 2.6552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.2076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.2681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.2094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.1497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.0375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.9844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.9366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.6992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6270e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5834e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4995e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4815e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.4499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4023e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3820e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.3740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.3700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.3661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3581e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3211e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.2972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.2940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2842e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.9047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3210 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 309/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4799e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4669e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4493e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4307e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4219e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3953e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3800e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3775e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3694e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.2200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3281 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 310/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.0289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.2746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2207e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.2017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.1578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.1524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.2706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4536e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.5208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.5764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0414e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9646e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8831e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.4361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3284 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 311/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.0078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.9549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.7332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.5698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.4463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.3313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.2189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.9968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.9210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.9144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.9069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.8982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.8805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.8715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.8632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.8565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.8512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.8630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.8702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.8772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.3868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3330 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 312/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.4003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.2354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.6005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.6228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.5976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.5279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.4699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.3891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.3044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.1014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.0793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.0621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.0467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.0362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.0259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.0188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.0146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.0062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.0006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.9639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.9128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.9093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.9062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.9034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.9013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.8956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.8932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.8912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.8892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.8869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.8842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.8816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.8792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.8767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.8743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.8724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.8707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.8642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.8597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.8572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.8547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.8524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.8500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.8476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.8433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.8389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.8242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.8215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.8189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.8165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.8142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.8117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.8091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.8064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.8051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.8019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.8001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.6373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3377 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 313/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 7.3788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 7.3388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 7.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.0165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.8207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.7014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.6320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.5527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.4129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.3687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.3268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.2951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.2740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.2614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.4584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.6155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.9555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 7.2375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 7.6117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 7.9271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.1952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.4270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.6244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.7948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.9435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.6548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.6403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.6259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.6114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.5970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.5827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.5684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.5406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.5267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.5128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.4307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.4174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.4042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.3526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.3397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.3268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.3139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.3009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.2792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.2683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.9723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3393 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 314/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 5.7302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 6.1435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 6.2762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 6.1462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.0381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.9652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.9036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.8465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.8159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.7889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.7814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.7758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.7772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.7829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.7893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.7845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.7827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.7806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.7746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.7641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.7546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.7369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.7327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.7320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.7336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.7568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.7761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.7930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 5.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 5.8295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.8485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.8590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.8685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.8781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.8875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.8956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.9019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.9069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.9114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.9146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.9162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.9174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.9187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.9193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.9201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.9204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.9199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.9190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.9175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.9159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.9058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.9028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.8996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.8990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.8982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.8974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.8962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.8948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.8932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.8914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.8867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.8843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.8820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.8798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.8758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.8736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.8711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.8685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.8632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.8602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 5.8572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.8513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.8483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 5.8463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.8443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.8420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 5.8396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.8371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.8346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.8320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 5.8292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.8263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.8237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 5.8210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.8184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.8161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 5.8138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.8035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.8007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.7978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.7948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.7919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.7889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.7859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.7832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.7805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.7777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.7749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.7720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.7691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.7660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 5.3797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3421 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 315/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.1809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.4360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.5740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.5095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.4339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.3640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.3215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.3044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.2810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.2609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.2247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.2260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.2318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.2309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.2310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.2295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.2253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.2210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.2177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.2166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.2182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.2221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.2243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.2248e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.2301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2425e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.3410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.3464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.3516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 4.3566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 4.3614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 4.3661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 4.3706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 4.3751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.3792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.3831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.3869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.3904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.4651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.5373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.6072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.6749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.9260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.9843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.0410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.0961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 5.1494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.2012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.2514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 5.3002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.3476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.3936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 5.4385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.4822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.5247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.5660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.6062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.6454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.6837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.7919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.8262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.8596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 9.8276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3465 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 316/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.1620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.0241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.0198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.5555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.4679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.2099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.1390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.1341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.1305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.1260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.1591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.1883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.2966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.3449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.3436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.3423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.3394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.3357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.3334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.3316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.3247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.3154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.0260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3472 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 317/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.0307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.1670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.1900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.1238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.8993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.8969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.8943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.8917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.8889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.8861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.8835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.8810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.8787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.8699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.8677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.8655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.7757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3487 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 318/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.7593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 4.1186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 4.1404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.0446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.9586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.8542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.8064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.7550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.7141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.6896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.6661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.6161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.6101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.6046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.6015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.6000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.5894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.5849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.5801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.5378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.5352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.5325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.5301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.5274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.5248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.5225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.5206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.5184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5642e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.5397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3515 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 319/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.4230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.0825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.9500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.7359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.4043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.0323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.8435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.8156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.6189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.6081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.5062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.3975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.3926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.3876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.3825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.3777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.3730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.3683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.3641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.3600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.3562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3537 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 320/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.8101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.7956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.7734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.6914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.6806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.6752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.6725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.6652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.6585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.6512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.6443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3562 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 321/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.5085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.4619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3577 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 322/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.4501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.3043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.2086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.2002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.1855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.1842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.1819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.1584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.1580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.1576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.1570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.1563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.1551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.1533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.1523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.1504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.1508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.1773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3601 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 323/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.5484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.5413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.5161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.4361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3614 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 324/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.1469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.1440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.1436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.1412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.2047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.2262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.2464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.2649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.9895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3651 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 325/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.6028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.7012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.7161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.6624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.6175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.5209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.5907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.5961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.6004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.6039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.6070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.6104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.6139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.6183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.6208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.6229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.6242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.6252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.6253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.6249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.6249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.6251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.6247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.6242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.6259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.6274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.6281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.6291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.6298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.6305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.6300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.6300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.6299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.6305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.6316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.6326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.6331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.6333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.6334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.6334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.6324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.6317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.6313e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.6051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.6042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.6000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.5747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.5735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.5723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.5710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.5697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 2.4211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3629 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 326/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.3313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.3426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.3009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.2795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.2049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.1833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.1668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.1509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.1444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.1389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.1360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.1303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.1166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.1106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.0977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.0761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.0657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.0618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.0578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.0538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.0537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.0529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.0520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.0515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.0509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.0502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.0500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.0403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.0398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.0397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.0425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.0449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.0471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.0492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.0513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.0532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3640 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 327/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.9786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.9977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.9147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.8349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3667 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 328/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.7453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.7436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.7419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.7402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.7385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.7369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.7189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.7179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.7168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.7158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.7149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.7139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.7090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.7080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.7070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.7061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.7042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.7033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.7024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.7014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3686 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 329/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.4680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.5025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.4799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.4560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.3830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.3802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.3773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.3746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.3575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.3560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.3549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.3536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.3524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.3516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.3486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.3478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.3469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.3461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.3446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.3438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.3438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.3442e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.4449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.4477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.4503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.4605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.4628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.4650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.4746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.4762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.4778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.4793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.4808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.4822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.4836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.4861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.4884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.4906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.4927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.4966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.4984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.5001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.5035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.5051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.5067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.5082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.5097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.5111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.5124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.6435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3708 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 330/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.2232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.6348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.6215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.5332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.5295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.5261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.5218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.5192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.5162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.5122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.5074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.5022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.4972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.4920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.4874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.4831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.6469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.7722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.9040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.0710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.3757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.3132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.6709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.9413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.8798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.7818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.7153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.8444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.8080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.5608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.1413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.1253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.2517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1318e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3044e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4959e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9360e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1712e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4354e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7166e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0143e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3314e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0201e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3885e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5612e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9820e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.4058e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.8349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2650e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.6965e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.1351e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.5730e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.4550e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.8990e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0344e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0790e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1238e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1682e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2121e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2557e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2994e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3428e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3859e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4289e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4719e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5145e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 6.5793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 - val_accuracy: 0.9759 - val_loss: 0.2666 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4982 Epoch 331/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9997 - loss: 9.9280e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9997 - loss: 9.8608e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9997 - loss: 9.8001e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9997 - loss: 9.7414e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9997 - loss: 9.6813e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9997 - loss: 9.6197e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9997 - loss: 9.5590e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9997 - loss: 9.5006e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9997 - loss: 9.4441e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9997 - loss: 9.3882e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9997 - loss: 9.3350e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9997 - loss: 9.2859e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9997 - loss: 9.2388e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9997 - loss: 9.1918e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9997 - loss: 9.1446e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9997 - loss: 9.0988e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9997 - loss: 9.0549e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 9.0115e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 8.9696e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 8.9278e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9997 - loss: 8.8859e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9997 - loss: 8.8437e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9997 - loss: 8.8024e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9997 - loss: 8.7625e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9997 - loss: 8.7235e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9997 - loss: 8.6841e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9997 - loss: 8.6456e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9997 - loss: 8.6073e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9997 - loss: 8.5693e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9997 - loss: 8.5318e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9997 - loss: 8.4953e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9997 - loss: 8.4592e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9997 - loss: 8.4235e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9997 - loss: 8.3878e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9997 - loss: 8.3529e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 0.9997 - loss: 8.3194e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9997 - loss: 8.2867e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9997 - loss: 8.2539e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9997 - loss: 8.2209e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9997 - loss: 8.1880e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9997 - loss: 8.1558e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9997 - loss: 8.1239e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9997 - loss: 8.0931e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9997 - loss: 8.0636e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9997 - loss: 8.0339e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9997 - loss: 8.0039e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9997 - loss: 7.9739e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9997 - loss: 7.9441e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9997 - loss: 7.9162e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9997 - loss: 7.8892e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9997 - loss: 7.8626e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9997 - loss: 7.8363e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9997 - loss: 7.8099e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9997 - loss: 7.7838e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9997 - loss: 7.7578e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9997 - loss: 7.7324e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 0.9997 - loss: 7.7085e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9997 - loss: 7.6850e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9997 - loss: 7.6616e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9997 - loss: 7.6386e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 0.9997 - loss: 7.6161e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9997 - loss: 7.5940e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9997 - loss: 7.5726e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9997 - loss: 7.5519e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9997 - loss: 7.5318e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9997 - loss: 7.5118e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9997 - loss: 7.4922e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.4730e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.4546e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.4364e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9997 - loss: 7.4186e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9997 - loss: 7.4016e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9998 - loss: 7.3853e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9998 - loss: 7.3694e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9998 - loss: 7.3538e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9998 - loss: 7.3386e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9998 - loss: 7.3240e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 7.3103e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 7.2975e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 7.2849e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9998 - loss: 7.2725e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9998 - loss: 7.2602e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9998 - loss: 7.2481e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9998 - loss: 7.2368e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9998 - loss: 7.2262e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9998 - loss: 7.2168e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9998 - loss: 7.2077e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 7.1988e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 7.1900e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 7.1813e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9998 - loss: 7.1732e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 7.1659e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 7.1594e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9998 - loss: 7.1530e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 7.1465e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 7.1400e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 7.1337e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9998 - loss: 7.1277e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9998 - loss: 6.4117e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 - val_accuracy: 0.9756 - val_loss: 0.2905 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4984 Epoch 332/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 0.9997 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9997 - loss: 9.9663e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9997 - loss: 9.8570e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 0.9997 - loss: 9.7542e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9997 - loss: 9.6497e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 0.9997 - loss: 9.5433e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9997 - loss: 9.4376e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 0.9997 - loss: 9.3402e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9997 - loss: 9.2508e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9997 - loss: 9.1630e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 0.9997 - loss: 9.0756e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9997 - loss: 8.9904e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9997 - loss: 8.9053e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9997 - loss: 8.8207e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 0.9997 - loss: 8.7379e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9997 - loss: 8.6577e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9997 - loss: 8.5802e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 0.9997 - loss: 8.5040e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9997 - loss: 8.4299e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9997 - loss: 8.3565e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 0.9997 - loss: 8.2834e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9997 - loss: 8.2112e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9997 - loss: 8.1401e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9997 - loss: 8.0719e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 0.9997 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9998 - loss: 6.7898e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9998 - loss: 6.7427e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9998 - loss: 6.6961e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9998 - loss: 6.6500e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9998 - loss: 6.6046e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9998 - loss: 6.5597e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9998 - loss: 6.5153e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9998 - loss: 6.1801e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9998 - loss: 6.1405e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9998 - loss: 6.1015e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9998 - loss: 6.0630e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9998 - loss: 6.0249e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9998 - loss: 5.9874e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9998 - loss: 5.9503e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9998 - loss: 5.6703e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9998 - loss: 5.6372e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9998 - loss: 5.6045e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9998 - loss: 5.5723e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9998 - loss: 5.5404e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9998 - loss: 5.5089e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9998 - loss: 5.4778e-04 - mean_absolute_error: 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mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9998 - loss: 5.2421e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9998 - loss: 5.2142e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9998 - loss: 5.1866e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9998 - loss: 5.1593e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9998 - loss: 5.1323e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9998 - loss: 5.1057e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9998 - loss: 5.0793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 5.0532e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 5.0275e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 5.0020e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9998 - loss: 4.9768e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9998 - loss: 4.9519e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9998 - loss: 4.9272e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9998 - loss: 4.9029e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9998 - loss: 4.8787e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9998 - loss: 4.8549e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9998 - loss: 4.8313e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9998 - loss: 4.8080e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9999 - loss: 2.0312e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9771 - val_loss: 0.3125 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 333/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.4854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.1792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.9731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.7192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.0928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.0149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7980e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.6034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.5010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1706e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.0954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9599e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.8946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.8878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.8810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8414e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7869e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6960e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6814e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3254 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 334/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4752e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4668e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4598e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4481e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.4446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.4434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.4425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - 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1.4464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.4436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.4433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.4428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.4422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.4416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4577e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4730e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4995e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.5190e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.5208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.5225e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5372e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5383e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5355e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.4856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3310 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 335/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3726e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1834e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1757e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1467e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1139e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1114e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1072e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1040e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3363 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 336/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.9934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.8788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.7937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.7115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.6463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.6005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.5679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.5280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.4846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.4423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.3986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.3562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.3109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.2696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.2343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.1997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.1704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.1446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.8914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.8779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.8163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.8040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.7806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.7686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.7570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.7156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.7052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.0139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.0375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.0597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.0801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.0992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.2767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.2942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.5252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.5530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.5799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.8080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.8270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.8633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.8808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3334 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 337/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.8982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.8011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.6789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.4141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.4053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.3843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.3625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.3378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.3135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.2889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.2625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.2360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.2126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.1882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.1652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.1271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.0940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.0771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.0589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.0425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.0238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.0062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.9891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.9132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.8993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.8863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.8741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.8485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.8355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.8231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.8110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.7998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.7895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.7800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.7699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.7602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.7505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.7407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.7312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.7209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.7106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.7009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.6905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.6806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.5954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.5862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.5769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.5347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.5262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.5182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.5101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.5017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.4933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.4852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.4768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.4687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.4608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.4378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.4001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.3934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.3866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.3798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.3732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.3668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.3262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.3128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.3060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.2993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.2733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.2601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.2535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.4247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3421 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 338/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.5602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.5087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.4526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.9746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.9199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.8355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.6403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.6218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.5994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.5779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.5587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.4700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.3633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.3349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.3273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.3190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.2016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.1913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.1865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.1814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.1766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.1718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.1669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.1623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.1575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.1355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.1311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.1266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.1144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.1104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.1062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.0945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.0906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.0742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.0700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.0657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.0574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.0532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.0491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.0451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.0250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.0167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.0126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.0085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.0045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.0007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.9969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.9932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.9894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.9855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.9818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.9779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.5085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3465 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 339/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.4358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.6835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.7515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.5992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.3569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.2475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.0015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.8203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.8173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.8147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.8117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.8233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.8261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.8285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.8306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.8393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.8409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.8422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.8586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.3725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3504 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 340/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.4770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.5941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.5094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.6727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.5887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.5480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.5103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.4699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.4384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.3971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.3772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.3562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.3390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.3217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.3038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.1772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.1613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.1285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.1123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.0069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.9955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.9489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.9380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.9270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.9161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.9058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.6656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.6616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.6580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.3683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3520 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 341/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.2757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.3303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.3805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.4178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.4894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.5083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.4997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.4900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.4866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.4793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.4688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.4626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.4512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.3970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.3800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.3635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.3497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.3348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.3214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.3018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - 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4.2266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.2188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.2112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.2044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.1984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.1920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.1863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.1807e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.5490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.5501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.5509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.5517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.5523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.5528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.5532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.5536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.5540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.5540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.5537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.5533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.5530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.5525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.5519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.5514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.5509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.5503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.5496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.5489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.5481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.5472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3551 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 342/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.4901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.5325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.3623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.3624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.3626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.3617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.3616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.3614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.3844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3571 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 343/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.4722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.7430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.5515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.3719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.2758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.2436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.2138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.1847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.1764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.1696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.1600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.1503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.1409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.1316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.1220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.1055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.0967e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.0523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.0427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.0393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.0357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.0325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.0298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.0275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.0246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.0216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.0191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.0163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.0132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.0102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.0045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.0000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.9983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.9970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.9956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.9940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.9924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.9908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.9825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.9808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.9792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.9778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.9762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.9745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.9727e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.9481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.9466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.9454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.9441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.9429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.9418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.9407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.9395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.9383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.9370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.9358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.9345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.9331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.9291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.9278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.9268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.9258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.9247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.9236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.9225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.9213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.9202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.9181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.9170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.9160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.9151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.9142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.9134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.9126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.9118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.9112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.9105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.9098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.9090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.9083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.9076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.9069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 2.8232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3595 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 344/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.2612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.2795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.9135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.3853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.3167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4788e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4208e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.2520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.2419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.2320e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.2222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.2126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.2032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.1940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.1850e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.1762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.1676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1591e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.1507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.0890e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.0819e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.0750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.0681e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.0614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.9522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.8980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.8446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.7934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.7430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.6932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.6442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.5483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.5017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.3655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.3214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.1930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.0709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.0315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.9927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.9544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.9165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.8792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.8423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.8060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.7701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.7345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.6994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.6681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.6371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.6065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.5762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.5463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.5167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.4874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.0333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3591 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 345/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.8309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.0514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.6372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.2986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.0523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.6151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.1184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.7044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.6659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.6277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.2241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.1999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.1767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.1538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.1316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.1100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.0481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.0315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.8968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.8828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.8689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.8553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.8420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.8288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.8159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.7016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.5546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.5465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.5385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.5003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.7018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3618 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 346/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 307ms/step - accuracy: 1.0000 - loss: 2.8280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.8939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.0160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.9112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 7.4595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.4241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6657e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.7464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.7554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.7592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.7585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.7543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.7475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.7289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.7053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.5136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.5006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2774e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1696e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1242e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.0946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.0889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.0833e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.0778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.0724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.0671e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0566e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0515e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0416e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0087e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.9977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.9541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.9112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.8688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.8269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.6645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.6250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.9887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3625 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 347/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.3913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.2978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.2750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.2538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.2185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.2060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.2111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.2243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.2371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.2397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.2379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.2551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.2591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.2589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.2577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3647 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 348/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.1943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.6711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 2.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - 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2.1360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1050e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.9273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3664 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 349/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.7144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.8769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.9342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.9565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.9624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.9572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.9558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3690 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 350/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.7207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.7189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.7172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3715 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 351/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3743 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 352/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.4685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.4332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3770 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 353/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.4338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3786 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 354/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3802 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 355/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 1.2191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3819 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 356/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.0809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.0826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3832 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 357/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.9483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.8162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.7152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.6432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.5654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.5107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.4796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.4714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.4155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.2871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.2725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.2544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.2346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.2134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.1093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.0909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.0737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.0591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.0487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.0241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.0165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.0076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.9994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.9902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.9815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.9744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.9670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.9605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.9554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.9535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.9504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.9465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.9427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.9397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.9362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.9186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.9163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.9066e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.8551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.8512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.8483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 8.8453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 8.8423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.8396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.8371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.8342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.8313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.8171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.8144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.8115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.8088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.8062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.8036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.7979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.7951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.7922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.7797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.7768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.7740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.7649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.7582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.7495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.4792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3849 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 358/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.9926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.9383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.8894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.8412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.7207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.4960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.4875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.4796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.3993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.3787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.3698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.3614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.3519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.3420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.3325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.3235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.3055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.2957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.2868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.2774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.2593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.2519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.2439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.2362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.2283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.2200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.2122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.2042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.1464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.1396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.1252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.1178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.1107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.1038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.9986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.9934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.9883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.9832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.9144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.9034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.2289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3865 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 359/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.6970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 8.7868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.8655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.1170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.1083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.1851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.2021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.2158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.1600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.1429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.1405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.1282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.1089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.0582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.7245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.0043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.9377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.3106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.6319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.9103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.7043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.8428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.0679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.6992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.6923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.6808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.6682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.6547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.6258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.6104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.4117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.3529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.3133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.6462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.6288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.6115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3827 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 360/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.1243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.1569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.1691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3837 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 361/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.1094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.9772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.9166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.7946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.7833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.7114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.6980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.6845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.6717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.6623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.6539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.6109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.6020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.5939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.5349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.5280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.5213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.5138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.5066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.4533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.4484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.4000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.3972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.3945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.3392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.3360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.3330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.6127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3855 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 362/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.8095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.3684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.6001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.9679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.9355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.9033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.8782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.8547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.8236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.8141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.8029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.7675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.7567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.7446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.7320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.7201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.7080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.6963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.6490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.6380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.6269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.6151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.5935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.5837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.5732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.5627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.5526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.5428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.5327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.5222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.5132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.5041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.4948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.4853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.3996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.3927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.3853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.3779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.3633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.3562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.3493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.3309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.2994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.2930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.2864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.2803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.2746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.2693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.2467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.2407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.2345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.2282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.2568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3881 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 363/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.1776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.5313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.6454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.6807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.5882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.4882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.4061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.3407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.2428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.1140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.1201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.1062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.1068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.1070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.1076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.1077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.0235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.0207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.0181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.0051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.0025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.6454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3877 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 364/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.3604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.9266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.3184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.3903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.4493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.4522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.4327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.3813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.3192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.2423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.2531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.2557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.2447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.2612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.2759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.3025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.3080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.3121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.3121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.3124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.3012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.2973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.2923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.2877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.2842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.2799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.2755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.2752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.2757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.2752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.2691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.2666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.2636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.2051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.2017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.2211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.2257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.2299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.2346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.2391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.2432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.2468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.2507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.2543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.2574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.2605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.2952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.2979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.3005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.3028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.3048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.3067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.3085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.3104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.3120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.3132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.3142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.3156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.3163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.3159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3894 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 365/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.6723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.2726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.2219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.9238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.6607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.4880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.3325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.1779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.0387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.7790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.7285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.6876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.6558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.6223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.5898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.5603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.5348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.5092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.3188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.1891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.1685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.0890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.8506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.8449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.8393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.8340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.0809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.7588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.9657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.3545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.7134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.3544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.5008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.0823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3893 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 366/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.5477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.2960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.1623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 9.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3912 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 367/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.9222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.1522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.3053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.2273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.1310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.0169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.6523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.6032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.5818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.5642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.5572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.5398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.5123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.4846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.4672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.4516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.4307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.4104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.3912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.3703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.3550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.3452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.3295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.3223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.3157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.3096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.3021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.2917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.2808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.2700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.2735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.2761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.2787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.2813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.2819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.2847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.2855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.2839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.2813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.2783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.2741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.2704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.2667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.2632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.2594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.2550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.2532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.7675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.0299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.2807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.5213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.5472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.1877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.9077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.4015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.8780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.6273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.2965e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0903e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2787e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4843e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7099e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9515e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2196e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5020e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7981e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1176e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4689e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8346e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6139e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0284e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4457e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8714e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.3043e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7418e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1775e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.6140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.0567e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5011e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9422e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 6.1430e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9757 - val_loss: 0.2759 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4983 Epoch 368/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9993 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9992 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9992 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9993 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9993 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4995  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9994 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9994 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9994 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9995 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9995 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9996 - loss: 0.0013 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4996  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9996 - loss: 0.0012 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9996 - loss: 0.0011 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9996 - loss: 0.0010 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9996 - loss: 9.9638e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9997 - loss: 9.8927e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9997 - loss: 9.8225e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9997 - loss: 9.7539e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9997 - loss: 9.6864e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9997 - loss: 9.6200e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9997 - loss: 9.5547e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9997 - loss: 9.4902e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9997 - loss: 9.4268e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9997 - loss: 9.3644e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9997 - loss: 9.3030e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9997 - loss: 9.2425e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9997 - loss: 9.1829e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9997 - loss: 9.1242e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9997 - loss: 9.0664e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9997 - loss: 9.0094e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9997 - loss: 8.9534e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9997 - loss: 8.8982e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9997 - loss: 8.8438e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9997 - loss: 8.7901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9997 - loss: 8.7371e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9997 - loss: 8.6849e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9997 - loss: 8.6335e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9997 - loss: 8.5828e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9997 - loss: 8.5328e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9997 - loss: 8.4834e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9997 - loss: 8.4347e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9997 - loss: 8.3865e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9997 - loss: 8.3389e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9997 - loss: 8.2920e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9997 - loss: 8.2455e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9997 - loss: 8.1997e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9997 - loss: 8.1545e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9997 - loss: 8.1098e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9997 - loss: 8.0657e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9997 - loss: 8.0223e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9997 - loss: 7.9793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9997 - loss: 7.9369e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9997 - loss: 7.8949e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9997 - loss: 7.8535e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9997 - loss: 7.8125e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9997 - loss: 7.7720e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9997 - loss: 7.7320e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9997 - loss: 7.6924e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9997 - loss: 7.6533e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9997 - loss: 7.6146e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9997 - loss: 7.5764e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9997 - loss: 7.5386e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9997 - loss: 7.5011e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9997 - loss: 7.4641e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9997 - loss: 7.4275e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9997 - loss: 7.3913e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9997 - loss: 7.3555e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9997 - loss: 7.3200e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9997 - loss: 7.2849e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9997 - loss: 7.2502e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9997 - loss: 7.2159e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 7.1819e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9999 - loss: 3.1369e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9771 - val_loss: 0.3141 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 369/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.7279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.2192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.9570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 7.6083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.3082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.0384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.8143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.4428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.3389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.1625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.9631e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.8968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.8306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.7678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.7066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.6462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.5865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.5605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.5334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.5043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.4763e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.4484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.4202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.3900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.3590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.3280e-06 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.9251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.8991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.8736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.8485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.8237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.7997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.6862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.6643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.6427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6000e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.5793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4815e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.3907e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.3738e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.3571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.3406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.3243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.3083e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.1957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.1823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.1691e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.1560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.1431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.1304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.1178e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.1053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.0441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.0322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.0204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.0088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.9974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.9861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.9749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.9638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.9527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.9417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.9308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.9199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.9092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.8986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.8880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.8776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8469e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8368e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.8268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.8169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.8070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.7972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.7876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.7780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.7686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.6407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3277 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 370/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.0642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.0872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.0983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0261e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9616e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6576e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5733e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5567e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5479e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.5191e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5134e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4935e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4837e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4817e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4682e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4620e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4604e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4513e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4498e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4450e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4326e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4294e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4140e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3984e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3366 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 371/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1043e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.8548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.7514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.6760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.6025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.5619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.5456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.5370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.5162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.4901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.4645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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9.2945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.2889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.2784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.2670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.2579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.2494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.2396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.2278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.2181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.2096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.2005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.1922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.0548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.0487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.0433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.0383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.0326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.0268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.0148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.0087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.8949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.8898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.7986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.7935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.7889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.7658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.7563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.7513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.7464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.7416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3424 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 372/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.3822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.3177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.8404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.6805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.5781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.4966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.3983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.3269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.1783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.1580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.1343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.1092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.9436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.9282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.8882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.8045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.7990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.7946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.7908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.7853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.7794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.7733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.7680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.7633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.7581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.7530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.7486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.7395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.7363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.7334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.7294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.7252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.7206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.7156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.7113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.7062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.7013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.6971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.6924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.6880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.6842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.6809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.6769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.6727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.6682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.6645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.6605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.6563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.6521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.6447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.6413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.5261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.5234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.5207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.5093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3467 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 373/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.6194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.1807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.9659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.7177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.5119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.3497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.2315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.1052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.9946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.9024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.6649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.6172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.5689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.5239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.1656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.1486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.1313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.1152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.1002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.0857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.0726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.0664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.0605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.0536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.0463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.0386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.0308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.0235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.0155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.0076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.0000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.9919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.9846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.9774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.9639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.8217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.8165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.8115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.8063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.7919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.7882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.7844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.7808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.7774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.7743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.7709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.7674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.7602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.7564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.7487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.7451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.7414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.7377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.7343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.7310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.7276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.7241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.7205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.7169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.7132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.7094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.7056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.7019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.6982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.6947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.6800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.6770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.6740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.2947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3504 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 374/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.9151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.3226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.1871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.0621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.9687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.8794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.6912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.6208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.5551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.4578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.3455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.3092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.2777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.2478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.2162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.1871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.1615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.1357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.1131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.0951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.0450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.0281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.0118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.9953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.9780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.9627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.9489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.9359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.9243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.9156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.9080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.8994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.8909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.8829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.8753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.8675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.8594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.8515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.8440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.8367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.8298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.8235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.8125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.8070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.8018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.7939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.7922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.7905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.7527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.7501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.7476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.7488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.7495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.7502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.7507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.7507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.7506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.7504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.7500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.7495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.7489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.7485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.7478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.7464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.7457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.7449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.7442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.7436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.7429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.7421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.7412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.7403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.7394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.7383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.7373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.7351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 4.6015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3542 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 375/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.3230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.3792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.3279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.1216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.9413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.7859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.6701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.5779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.3788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.3347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.3053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 4.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 4.2723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 4.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.2377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.2220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.2096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.1961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.1902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.1807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.1776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.1792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.1806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.1774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.1751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.1725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.1688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.1639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.1586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.0990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.0946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.0899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.0872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.0822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.0713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.0689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.0668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.0649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.0632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.0612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.0609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.0612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.0603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.0599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.0594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.0589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.0585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.0586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.0585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.0581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.0560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.0553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.0545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.0539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.0529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.0526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.0521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.0516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.0511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.0499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.0492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.0476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.0469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.0462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.0457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.0451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.0446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.0440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.0433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.0426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.0418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.0409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.0400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.0391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.0381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3567 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 376/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.2740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 4.4034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.3765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.2269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.0897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.8627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.7508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.7212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.6935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.6771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.6656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.6078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.5984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5746e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.4815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.4800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.4785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.4772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.4760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.4748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.4734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.4720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.4706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.4692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.4678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.4770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 295ms/step - accuracy: 1.0000 - loss: 3.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 295ms/step - accuracy: 1.0000 - loss: 3.5031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.5113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.5194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.5274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.5350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.5495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.5565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.5633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.5698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.5766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.5833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.5897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 4.3574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3536 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 377/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.2536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.4604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.4259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.2195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.0250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.9090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.8386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.7681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.6936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.6325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.5851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.5437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.5100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.4836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.4612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.4341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.4069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.3807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.3564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.3317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.3065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.2847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.1941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.1685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.1564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.1443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.1320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.1203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.9937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.9822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.9704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.9570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.9385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.9265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3592 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 378/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.5416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.4998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.3503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.3419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.2970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.2844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.2828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.3561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3646 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 379/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 3.3662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.7601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.7861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.5892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.5069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.2774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.2532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.2354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.2235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.1910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.0578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.0390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.0300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.0136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3643 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 380/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.3049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.1855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.1661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.9357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.6948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.6553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.6254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.5976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.5792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.5043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.4802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.4647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.4609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.4361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.4274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.4138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.4115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.4040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.4010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3792e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.3669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.3655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.3644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.3632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.3619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.3580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.3537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.3528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.3519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.3482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3668 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 381/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.7847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.6261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.5588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.4452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.3766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.3512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.3345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.3040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.3000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.2888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.2886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.2849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.2829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.2807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.2783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.2751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.2721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.2693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.2665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.2642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.2623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.2607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.2588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.2568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.2545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 2.2521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 2.2498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 2.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 2.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 2.2426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 2.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 2.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 2.2376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 2.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 2.2357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 2.2344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 2.2330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 2.2317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 2.2305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 2.2290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 2.2276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 2.2263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 2.2249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 2.2238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 2.2228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 2.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 2.2210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 2.2199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.2188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.2133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.2119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.2105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.2039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.2026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.2012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.1999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.1986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.1972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.1958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.1943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.1932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.1922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.1912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3694 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 382/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.7884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3718 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 383/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.7991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6462e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.7072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3736 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 384/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.2738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.1270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.6548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.6542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.6536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.5428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3763 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 385/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.0560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.9314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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1.6246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3771 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 386/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.8281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.8228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.7774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.6037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.5787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.5235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.4629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.4538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.4583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.4624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.4665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.4703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.4739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.4772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.4804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.4834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.4861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.4888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.4913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.4959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.4981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.5002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.5023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.5042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.5059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.5076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.5091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.5106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.5120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.5193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.5204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.5214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.5224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.5457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.5683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.6110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.6314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.6512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.6705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.6893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.7076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.7916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.8071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.8222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.8526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.8825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.9116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.9989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.0265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 2.1463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 2.2630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 2.3878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 2.5095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 2.6282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 2.7594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 2.8878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 299ms/step - accuracy: 1.0000 - loss: 1.8169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3703 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 387/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 7.1414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.6123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.9373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.3428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.2039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.7994e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.2065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.0015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.0385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.4053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.6930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.0609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.5495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.8426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.0979e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.2593e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4131e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.5497e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.7220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8837e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.0369e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1783e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3052e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.5032e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.7209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9583e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1852e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7087e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.9909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.3341e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.6708e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.0416e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.4231e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.8054e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.2177e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.6444e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.0885e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.5386e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.0140e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.5151e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.0375e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.5837e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0117e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0684e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1255e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1827e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2413e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3001e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3595e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4183e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4782e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.5387e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.6005e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.6630e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7245e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9999 - loss: 1.7876e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 0.9999 - loss: 1.8516e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 1.9145e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 1.9767e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 2.0382e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 0.9999 - loss: 2.0986e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9999 - loss: 2.1575e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9999 - loss: 2.2154e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 0.9999 - loss: 2.2725e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9999 - loss: 2.3287e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9999 - loss: 2.3843e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 0.9999 - loss: 2.4387e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9999 - loss: 2.4924e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9999 - loss: 2.5451e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9999 - loss: 2.5969e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 0.9999 - loss: 2.6473e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9999 - loss: 2.6968e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9999 - loss: 2.7453e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 0.9999 - loss: 2.7924e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9999 - loss: 2.8380e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9999 - loss: 2.8831e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9999 - loss: 2.9274e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 0.9999 - loss: 2.9706e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9999 - loss: 3.0127e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9999 - loss: 3.0540e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9999 - loss: 3.0942e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9999 - loss: 3.1336e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9999 - loss: 3.1717e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9999 - loss: 3.2091e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.2454e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.2808e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.3150e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.3483e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9999 - loss: 3.3810e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9999 - loss: 3.4134e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9999 - loss: 3.4449e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9999 - loss: 3.4756e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9999 - loss: 3.5055e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9999 - loss: 3.5347e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 3.5632e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 3.5911e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 3.6185e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 3.6454e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 3.6715e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 3.6968e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 3.7214e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 3.7457e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 3.7694e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 3.7926e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 3.8155e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 3.8381e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 3.8601e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 3.8814e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9999 - loss: 3.9023e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9999 - loss: 3.9228e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9999 - loss: 3.9429e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 3.9624e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 3.9814e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 4.0001e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 4.0183e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9998 - loss: 6.1885e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 - val_accuracy: 0.9762 - val_loss: 0.3038 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 388/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9998 - loss: 5.5269e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.9998 - loss: 6.0182e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9998 - loss: 6.0267e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9998 - loss: 5.9982e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9998 - loss: 5.9465e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 0.9998 - loss: 5.8638e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 0.9998 - loss: 5.7875e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9998 - loss: 5.7106e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 0.9998 - loss: 5.6366e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9998 - loss: 5.5880e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9998 - loss: 5.5793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 0.9998 - loss: 5.5574e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9998 - loss: 5.5246e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9998 - loss: 5.5004e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9998 - loss: 5.4750e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 0.9998 - loss: 5.4470e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 0.9998 - loss: 5.4174e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9998 - loss: 5.3950e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 0.9998 - loss: 5.3760e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9998 - loss: 5.3532e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9998 - loss: 5.3284e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9998 - loss: 5.3007e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 0.9998 - loss: 5.2752e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9998 - loss: 5.2519e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9998 - loss: 5.2318e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9998 - loss: 5.2117e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9998 - loss: 5.1926e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9998 - loss: 5.1732e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9998 - loss: 5.1534e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.1336e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.1169e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.1016e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9998 - loss: 5.0855e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 5.0687e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 5.0555e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9998 - loss: 5.0432e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 5.0309e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 5.0192e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9998 - loss: 5.0071e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 4.9954e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 4.9838e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 4.5595e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 4.5460e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 4.5324e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 4.5187e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 4.5048e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 4.4908e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 4.4769e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9998 - loss: 4.4629e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9999 - loss: 4.4489e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.9999 - loss: 4.4350e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9999 - loss: 4.4210e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9999 - loss: 4.4070e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 0.9999 - loss: 4.3930e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 4.3789e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 4.3647e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 4.3506e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 0.9999 - loss: 4.3363e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9999 - loss: 4.3221e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9999 - loss: 4.3078e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 0.9999 - loss: 4.2936e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9999 - loss: 4.2793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9999 - loss: 4.2650e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 0.9999 - loss: 4.2508e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 4.2365e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 4.2223e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 4.2080e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 0.9999 - loss: 4.1938e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9999 - loss: 4.1796e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9999 - loss: 4.1655e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 0.9999 - loss: 4.1514e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9999 - loss: 4.1373e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9999 - loss: 4.1233e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 0.9999 - loss: 4.1093e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9999 - loss: 4.0953e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9999 - loss: 4.0814e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9999 - loss: 4.0676e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 0.9999 - loss: 4.0538e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9999 - loss: 4.0400e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9999 - loss: 4.0263e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 0.9999 - loss: 4.0127e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9999 - loss: 3.9991e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9999 - loss: 3.9856e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9999 - loss: 3.9722e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 0.9999 - loss: 3.9588e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9999 - loss: 2.3644e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9772 - val_loss: 0.3343 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 389/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.5341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.5304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.5301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3332e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2329e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.0391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.9044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.6340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.6028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4282e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1440e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1020e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0883e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9235e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8926e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7996e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.7741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.7659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.7578e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.7497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.7417e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.7338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.7052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.7009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6967e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6880e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6703e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6615e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6485e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6315e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5889e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.1452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3413 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 390/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7124e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5696e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4112e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.4045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3695e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3595e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3333e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3306e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3130e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3103e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3006e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2941e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2690e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2630e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2445e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2343e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2327e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2061e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1858e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1844e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1802e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3488 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 391/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 9.9063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 9.9923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.9963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.7275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.2532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.3093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.3116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.2923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.2747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.2360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.1690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.1564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.8771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.8500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.8091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.7901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.7706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.7526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.7341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.7142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.6928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.6727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.6537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.6343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.6159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.6150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.6145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.6114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.6072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.6023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.5973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.6114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.6235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.6335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.6811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.6801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.6790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.6736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.6725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.6710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.6315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.6272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.6226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.5981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.5931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.5882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.5835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.5730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.5449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.5390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.5331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.5273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.5216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.5161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.5104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.5045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.7647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3528 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 392/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.0288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.1663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.8170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.7000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.2845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.1820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.1495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.1270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.1206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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6.0599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.0698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.0753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.0800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.0778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.1257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.1585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.1888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4805e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.5584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.5883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.5941e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.6269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.6297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.6328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.6359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.6387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.6412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.6451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.6467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.6480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.6489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.6497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.6504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.6509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.6514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.6518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.6523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.6524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.6524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.6523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.6520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.6515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.6507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.6499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.6454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.6443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.6431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.6410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.6397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.6337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 6.4417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3556 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 393/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 5.8868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 1.0000 - loss: 5.9475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 6.0724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.9314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.7918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.6759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.5826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.5296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 5.3842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.3450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.2950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 5.2790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.2670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.2567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.2473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.2274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.2177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.2038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.2002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.1981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.1964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.1882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.1872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.3531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.5025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.6378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.7616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.9780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.0733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.1612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.2501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.3318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.4068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.5911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.7012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.8079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.9280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.9772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.9616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4742e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.5384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.5988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.6559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.8089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.8546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8979e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0828e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.2002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2749e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3582e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3939e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4683e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.5045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5153e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5351e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5608e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5824e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.6004e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.6056e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.6106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.6151e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.6194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.6233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6415e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6437e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6457e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.7526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3567 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 394/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 6.4363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.5782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.6137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.3327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.2747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.1889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.1048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.0152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.9373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.6393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.6173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.5260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.5147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.5034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.4563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.4463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.4210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.4144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.4082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.4034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.3977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.3919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.3657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.3594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.3526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.3466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.3410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.3358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.3303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.3081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.2225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.2144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.1744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.1672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.1636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.1599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.0417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3628 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 395/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.7533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.1249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.6623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.2107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.8482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.6149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.4308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.2836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.1458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.0292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.9913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.9495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.8898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.8649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.7256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.0787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.5535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.5524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.5503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.5465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.4029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.1969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.1835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.1316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.1187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.1060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0548e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.9570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.9451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.9331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.9212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.9095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.8977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.8863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.8750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.8639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.8090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.7983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.7773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.7669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.7566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.7465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.7366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.7267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.7168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.7069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.5183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3656 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 396/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.9372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.1989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.3576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.2158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.0643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.9971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.9293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.8714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.8253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.7799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.6093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.4847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.4106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.4026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.3953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.3630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.3573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.3456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.3400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.3347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.3120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.3076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.3033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.2859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.2819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.2782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.2748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.2716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.2683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.2650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.2550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.2515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.2415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.2292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.2028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.2007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.2128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3695 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 397/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.9682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.1348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.8746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.8690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.8635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.8397e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.7561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.7550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.7537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.7514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.7504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.7463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.5963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3714 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 398/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.1331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.7091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.7038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.6452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.6402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.6352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.6308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.6268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.6227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.6115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.6123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.6129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.6134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.6137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.6139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.6141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.6141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.6137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.6131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.6124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.6116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.6109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.6101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.5997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.5986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.5973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.5961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.5950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.5938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.5926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.5913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.5899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.5886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.5892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.5899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.5858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.5853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.5846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.5840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.5834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.5827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.5820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.5813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 2.4962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3767 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 399/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.6510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.6067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.1954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.1859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - 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2.1194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.8898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3777 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 400/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.5747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.4248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.2642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.1832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.1192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.0710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.7488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3798 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 401/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.1297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.0445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.9178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3784 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 402/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.5710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.7214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.4942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.4848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.4754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.4669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.4585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.4501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.4417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.4338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.4261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.4185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.4110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 2.3832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.3764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.3697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.3630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 2.3563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.3498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.3371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.3311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.2970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.2915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.2860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.2806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.2595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.2447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.2256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.2118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.2031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3812 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 403/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.3447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.3772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3837 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 404/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.4853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.3900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.3778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.3694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.3748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.3771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.3781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.3751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.3727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.3734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 1.3751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 1.3742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 1.3724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 1.3715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 1.3703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 1.3682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 1.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 1.3627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.3552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.3537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.3527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.3509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3476e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.3171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.3148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.3139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.3129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.3120e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.2948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.2941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.2934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.2907e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.2858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.2852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.2835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.2830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.2810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.2804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.2793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.2782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.2777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.2772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.2767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.2762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.2757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.2752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.2746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.2741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.2735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.2730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.2724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.2719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.2081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3860 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 405/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.5644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.6088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.3826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.3731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.3635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.1446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3881 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 406/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.2158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.2212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.1968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.1743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.1367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.1181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.1114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.1069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.1020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.0999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.0989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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1.0835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.0835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.0829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.0779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.0765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.0753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.0741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.0733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.0727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.0697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.0689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.0680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.0661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.0652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.0644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.0637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.0632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.0626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.0620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.0586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.0578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.0571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.0565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.0559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.0549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.0544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.0538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.0532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.0527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.0481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.0578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.1166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.1176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.2205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3893 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 407/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.1212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.3000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.7272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.2801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.9164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.6141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.9566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.7915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3916 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 408/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.1209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.9653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.8643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.7905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.7411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.7051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.6639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.6258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.6029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.5787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.5503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.5175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.4869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.4648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.4400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.4201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.4039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.3989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.3908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.3743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.3646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.3549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.3425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.3291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 9.3198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.3097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.3000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 9.2911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.2829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.2731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.2627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 9.2525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.2424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.2319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.2205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.2090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.1979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.1872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.1773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.1683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.1621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.1545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.1472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.1401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.0954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.0892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.0839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.0790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.0741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 9.0692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.0643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.0595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.0552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 9.0505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.0458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.0417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 9.0375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.0336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.0304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 9.0238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.0203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.0167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 9.0131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.0093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.0053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 9.0012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 8.9973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 8.9931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 8.9892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 8.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 8.9821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 8.9785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 8.9746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 8.9707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 8.9670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 8.9630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.9546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.9506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.9466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.9431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.9398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.9366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.9334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.9271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.9218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.9190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.9165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.9139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.9113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.9063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.9041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.9016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.8990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.8963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.8935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.8905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.8876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.8846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.8817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.8788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.5276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3932 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 409/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.9703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.8388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.7046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.3886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.2876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.2581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.2333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.2117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.8947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.7347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.7281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.7215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.7151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.7091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.7028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.6772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.6741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.6710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.6552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.6517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.6493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.6469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.6441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.6411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.6381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.6349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.6319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.6287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.6252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.6224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.6193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.5975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.5950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.5925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.5900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.5874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.5852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.2495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3953 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 410/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.1450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.4959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.1077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.1944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.9655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.8547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.5279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.2314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.7460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.6943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.6434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.4065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.2790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.1621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.1248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.9229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.6992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.6890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.6824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.6754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.6682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.6607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.6531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.6453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.5160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.5105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.5050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4035 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 411/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.1313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.1285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.1271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.1265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.1209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.0981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.0159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4001 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 412/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.2770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.2744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.2713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3988 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 413/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.1343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.9783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.8558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.7127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.5804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.4676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.3607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.3377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.2979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.2629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.2236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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8.9869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.9766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.9623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.9467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.1866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.1866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.1855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.1833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.1806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.1781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.1340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.1223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.1199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.1169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.1061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.1022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.0986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.0866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.2277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4014 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 414/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.4741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.2829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.9115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.8246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.7601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.6904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.6299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.5960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.5516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.5271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.5129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.5021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.4796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.2972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.2830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.1968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.1886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.1797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.1718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.1691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.1657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.1632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.1607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.1586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.1563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.1546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.1544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.1539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.1591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.1631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.1660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.1715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.1739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.1785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.1809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.1828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.1848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.1849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.1844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.1848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.1852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.1845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.1838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.1831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.1768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.1776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.1785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.1883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.1903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.1925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.2021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.2017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.2015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.2301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.2579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.2852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3970 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 415/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.8319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.0119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.9386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.5925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.2498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.9770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.7907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.6816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.5857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.5652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.5924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.8134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4002 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 416/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 9.7157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.4231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.0164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.7354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.4995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.3831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.0512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.8484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.8522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.8580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.8630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.8700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.8727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.8716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.8694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.8645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.8577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.8184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.8114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.7961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.7865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.7775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.7681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.7603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.7517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.7439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.7158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.7093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.7026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.6883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.6758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.6688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.6617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.6551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.6481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.6414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.6351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.6316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.6283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.6249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.6211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.6172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.6128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.6081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.6035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.5862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.5823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.5778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.5732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.5682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.5631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.5382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.5333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.5288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.5243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.5198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.5155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.5111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.5068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.5023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.4822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.4795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.4771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.4746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.4720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.4694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.4666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.4636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.4605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.4573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.1044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4048 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 417/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.4358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.1362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.9001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.8021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.6017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.4222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.2487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.0629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.8868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.7376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.6240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.5478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.4979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.4653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.4317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.3501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.3090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.2700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.2312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.1526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.1215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.0902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.0618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.0356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.0143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.8102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.6964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.6849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.6734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.6623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.5409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.5270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.5250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.5234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.5214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.5191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.5169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.5145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.5001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.4973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.4947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.4839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.4809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.4782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.4751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.4722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.4695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.4669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.4644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.4622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.4602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.4580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.4556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.4534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.4511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.4489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.4466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.4442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.4420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.4399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.4380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.4361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.4342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.4321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.4299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.4277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.4255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.4235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.4215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.4193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.4172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 6.4150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 6.1503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4068 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 418/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.6221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.0283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.2000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.4425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.2808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.2219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.2379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.2989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.6267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.6869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.7882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.8275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.8611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.8904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.9148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.0055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.1243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.1375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.1869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.1931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.1980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.2017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.2051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.2085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.2108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.2127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.2140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.2150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.2152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.2146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.2135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.2121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.2103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.1934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.1916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.1892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.1864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.1833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.1638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.1595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.1551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.1039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.0991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.0943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.0701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.0653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.0606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.5492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4080 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 419/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.3233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.7966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.9708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.9750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.9471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.8819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.8399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.7980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.7669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.7418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.7160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.7021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.7281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.7827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.8019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.8203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 4.8537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.8604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.8685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.8746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.8815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 4.8884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 4.8962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 4.9011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 4.9066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 4.9124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 4.9178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.9210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.9236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.9273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 4.9304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 4.9341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 4.9384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 4.9433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.9496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.9542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.9593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.9632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.9663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.9716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.9755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.9793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.9870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.9903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.9937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.9975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.0006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.0029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.0047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.0062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.0107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.0112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.0119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.0204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.0208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.0210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.0267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.0279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.0289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.0299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.0310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.0318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.0326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.0334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.0345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.0362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.0369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.0377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.0384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.0390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.0425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.0429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.0432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.0448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4099 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 420/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.4734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.5299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.4602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.3375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.1151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.9109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.7509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.1488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.8600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.7271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.9787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0319e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.9408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.5703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.4935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.4183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.3430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.8308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.6910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.6224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.4903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.4256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.3619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.1774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.1176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.0587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.0006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.9431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.8867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.8313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.7766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.7228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.6698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.6178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.1385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.8869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.8470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.8076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.7687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.4768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.4426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.4087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.3753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.3423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.3097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.2775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.2457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.2143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.9765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4115 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 421/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.1301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.9206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.4306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.0983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.8420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.6088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.4029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.2289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.0885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.8633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.7801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.7036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.6264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.5570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.4969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.4409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.4041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.3679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.3363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.3106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.2648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.2464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.2290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.2124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.1771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.1666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.1583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.1480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.1379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.1303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.1063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.0989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.0911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.0837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.0767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.0695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.0623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.0542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.0263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.0164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.0115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.0060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.9941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.9456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.9438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.9422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.9403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.9384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.9362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.9340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 6.9319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.9123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.9103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.9080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.9059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.9036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.9012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.8961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.8939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.8913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.8804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.8774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.8741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.8712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.8682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.8651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.8621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.8591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.8563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.8542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.8449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.8423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.5139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4115 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 422/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.4629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.3732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.5407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.5052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.4471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.4306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.4121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.3592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.3347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.3192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.3293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.3678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.4012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.4188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.5064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.5262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.5401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.5561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.5684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.5865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.6027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.6177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.6308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.6417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.6505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.6596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.6669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.6800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.6916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.7026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.7121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.7207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.7282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.7355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.7408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.7449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.7483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.7505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.7517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.7517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.7521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.7613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.7640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.7662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.7678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.7686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.7690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.7691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.7686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.7658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.6984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.6947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.6912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.6876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.6842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.6808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.1720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4131 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 423/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.0216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.6691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.4373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.4168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.3289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.2787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.2213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.1760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.1185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.0825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.2264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.2676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.3045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.3360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.4001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.2664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.2616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.2565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.2512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.2459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.2405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.2198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.2159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.2119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.1992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.1943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.1893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.1842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.1790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.1738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.1688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.1641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.1595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.1454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.1271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.1052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.1016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.0672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.0515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.0279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.0135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.0024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.5491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4149 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 424/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.0151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.2976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.4433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.4636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.8296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.8980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.9109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.9076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.9068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.9003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.9207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.9179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.9151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.9101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.9052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.9003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.8326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.8334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.8345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.8348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.8352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.8351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.8344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.8318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.8300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.8278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.8180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.8155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.8129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.8101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.3550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4169 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 425/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.6531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.9581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.3472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.8612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.5051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.2124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.9629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.5848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.4449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.7777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.7282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.6833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.6472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.6129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.4164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.3698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.3327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.3206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.3092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.1240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.1191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.1143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.1047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4185 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 426/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.7925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.5963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.5735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.5549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.5499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.5394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.5705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.5704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.5522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.5497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.5486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 3.5474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.5460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.5439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.5416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.5396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 3.5312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 3.5279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.5254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.5224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.5202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.5181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.5169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.5152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.5134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 3.5115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.5098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.5081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.5060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.5041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 3.5024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.5004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.4987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 3.4970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.4953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.4934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 3.4918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.4892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.4880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 3.4866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 3.4851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 3.4836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 3.4819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 3.4803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 3.4798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 3.4794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.4788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.4781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.4776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 3.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.4764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.4755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 3.4746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.4737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.4731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.4727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 3.4723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.4721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.4718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 295ms/step - accuracy: 1.0000 - loss: 3.4715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.4711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.4706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 295ms/step - accuracy: 1.0000 - loss: 3.4700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.4694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.4688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.4684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.4678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.4672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.4666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.4661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 3.4654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 3.4647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 3.4641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 295ms/step - accuracy: 1.0000 - loss: 3.4635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 3.4628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 3.4621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 295ms/step - accuracy: 1.0000 - loss: 3.4613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 295ms/step - accuracy: 1.0000 - loss: 3.4605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.4599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.4595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.4591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 295ms/step - accuracy: 1.0000 - loss: 3.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 295ms/step - accuracy: 1.0000 - loss: 3.4588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 295ms/step - accuracy: 1.0000 - loss: 3.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 3.4581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 3.4576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 295ms/step - accuracy: 1.0000 - loss: 3.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 3.4566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 3.4561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 3.4557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 1.0000 - loss: 3.4553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 299ms/step - accuracy: 1.0000 - loss: 3.4021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4204 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 427/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.7824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.5795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.4640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.2840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.0076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.9368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.8572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.7851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.7256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.6914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.6055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.5836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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3.4369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4604e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4863e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.4743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.4733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.4722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.4406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.4394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.4381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.2765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4222 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 428/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.0838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.5309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.0203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.0215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.0217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.0217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.0217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.0198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4235 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 429/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.7312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.8256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.9695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.9655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.0026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.0073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.0117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.0157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.0698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.0698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.0697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.0695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.0692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.0614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4247 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 430/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.0245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 37s 316ms/step - accuracy: 1.0000 - loss: 3.0014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 36s 308ms/step - accuracy: 1.0000 - loss: 2.9750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 305ms/step - accuracy: 1.0000 - loss: 2.8725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 304ms/step - accuracy: 1.0000 - loss: 2.7872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 303ms/step - accuracy: 1.0000 - loss: 2.7217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 303ms/step - accuracy: 1.0000 - loss: 2.6950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 2.6674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 2.6365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 2.6063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 2.5888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 2.5698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.6418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.0838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.1021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.9244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4247 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 431/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 307ms/step - accuracy: 1.0000 - loss: 2.5104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.4528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.5879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.5414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.4936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.4293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.3681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.3129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.2680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.2230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.1842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.1509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.1201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.0930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.0702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.0483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.0267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.0058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.0042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.0159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.0262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.0404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.0520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.0613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.1010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.1041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.1066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.1123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.0648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.0342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.0323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.0303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.0284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.0265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.0246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.0227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.0206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.0185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.0143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.0123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.0081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.0060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.0039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.0018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.7271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4269 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 432/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.4841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.4724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.2001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.1977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4280 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 433/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.7480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.6644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.5413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.4951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.4765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.4630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.2028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.2510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4295 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 434/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.3977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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2.4189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.4017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.3980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.3939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.3901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.3860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.3820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4311 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 435/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.7562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.8217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.7639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.6848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.6281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.5744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.5265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.3812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.3555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.3315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 2.3112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 2.2904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 2.2705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 2.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 2.2349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 2.2191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 294ms/step - accuracy: 1.0000 - loss: 2.2026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 2.1868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 2.1734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 294ms/step - accuracy: 1.0000 - loss: 2.1604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 27s 294ms/step - accuracy: 1.0000 - loss: 2.1487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.1381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.1288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.1096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.1005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.0913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.0831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.0751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.0684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.0622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.0563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.0510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 2.0493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.0479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.0459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 2.0417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 2.0398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 2.0379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 2.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 2.0331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 2.0307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 2.0285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 2.0263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 2.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 2.0221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.0198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.0173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.0147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.0126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.0045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.0011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4322 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 436/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.6456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.1616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.9151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.9015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.8886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4335 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 437/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.4802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.0878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.1913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.5531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.1137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.0428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.6358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.6030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0427e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4896e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.4479e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.4764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5299e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9528e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9244e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4939e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.1496e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.9209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.6403e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.6279e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.7597e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0863e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2020e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3193e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4361e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5544e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6747e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9999 - loss: 1.7965e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9999 - loss: 1.9169e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9999 - loss: 2.0366e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9999 - loss: 2.1575e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9999 - loss: 2.2779e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9999 - loss: 2.3953e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9999 - loss: 2.5099e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9999 - loss: 2.6228e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9999 - loss: 2.7330e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9999 - loss: 2.8395e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9999 - loss: 2.9436e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9999 - loss: 3.0458e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9999 - loss: 3.1462e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9999 - loss: 3.2441e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9999 - loss: 3.3398e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9999 - loss: 3.4333e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9999 - loss: 3.5238e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9999 - loss: 3.6107e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9999 - loss: 3.6950e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9999 - loss: 3.7765e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9999 - loss: 3.8558e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9999 - loss: 3.9326e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9999 - loss: 4.0072e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9999 - loss: 4.0795e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9999 - loss: 4.1493e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9999 - loss: 4.2169e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9999 - loss: 4.2822e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9999 - loss: 4.3455e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9999 - loss: 4.4066e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9999 - loss: 4.4654e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9999 - loss: 4.5221e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.5767e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.6296e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.6805e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9999 - loss: 4.7300e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 4.7778e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 4.8240e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9998 - loss: 4.8685e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 4.9113e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 4.9525e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 4.9924e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 5.0307e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 5.0676e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 5.1031e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9998 - loss: 5.1374e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 5.1705e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 5.2022e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 5.2327e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 5.2621e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 5.2903e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 5.3176e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.3441e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.3695e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.3939e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 5.4173e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 5.4397e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 5.4612e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 5.4819e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 5.5017e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9998 - loss: 5.5208e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 5.5392e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 5.5569e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 7.6605e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 - val_accuracy: 0.9763 - val_loss: 0.3279 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4987 Epoch 438/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2032e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4635e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4346e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4811e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4914e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4763e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4574e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4311e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4014e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3759e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3526e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3372e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3243e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3127e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2989e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.2837e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2684e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2530e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2393e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2253e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2120e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1987e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1862e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1767e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1671e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1573e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1473e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1376e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1278e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1188e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1095e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1002e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0912e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0821e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0730e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0641e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0551e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0462e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0374e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0286e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0200e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0114e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0029e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.9461e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.8636e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.7823e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.7022e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.6232e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5453e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4684e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.3926e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.3177e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.2439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.1710e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0992e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0284e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9586e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8898e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8220e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7552e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6895e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6248e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.5611e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.4982e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.4363e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3152e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2559e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.1975e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1400e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0833e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0275e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.9724e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.9182e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.8648e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.8121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7602e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7090e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.6585e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.6088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5597e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5113e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.4637e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.4166e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3703e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3245e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2794e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1910e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1477e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1050e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.0628e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.0212e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9801e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9396e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.8995e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.8601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.8211e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7826e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7446e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6700e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6334e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5973e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5616e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5263e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.4914e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.4570e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.4230e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.3894e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.3562e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.3233e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.2909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.2588e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.2271e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1958e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1648e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1342e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1039e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0740e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5103e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3483 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 439/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.4265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.5143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.5652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5245e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5028e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4600e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4117e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4073e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4044e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3991e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3915e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3872e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3841e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3809e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3792e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3731e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3638e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3539e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3524e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3397e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3381e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3284e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3051e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3024e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2950e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2937e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2912e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2900e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3568 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 440/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.9850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.1760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.3304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.0318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 8.9503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 8.8511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 8.7465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 8.6468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 8.5668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 8.5282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 8.5067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 8.4949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 8.4977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 8.4978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 8.4896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 8.4773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 8.4598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 8.4430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 8.4238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 8.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.3779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.3758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.3732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 8.3726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.3720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.3703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 8.3657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.3592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.3507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.3409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.4854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.5468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.6034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.6536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.7422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.7813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.8157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.8463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.8734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.8978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.9553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 8.9825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.0076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.0313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.0781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.0981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.1322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 9.1467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 9.1907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.1994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 9.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.2221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.2280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.2328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.2365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.2415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.2425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.2434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.2442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.2446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.2456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.2438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.2397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.2371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.2248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.2229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.2209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.2086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.2053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.2019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.1983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.1947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.1912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.1876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.1704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.1656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.1606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.1450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.1184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.1127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.1069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.1009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.0763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.3414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3618 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 441/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 8.4039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 8.0420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 7.5128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 7.2757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.9879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.8921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.8006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.7205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.6688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.6198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.5843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.5612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.5427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.5184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.4937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.4715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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6.4011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.3979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.3950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.3817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.3754e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 6.1883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 6.1833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 6.1786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 6.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.1692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.1649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 6.1606e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.1235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.1187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.1141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 6.1099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.1014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 6.0971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.0928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.0885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 6.0843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.0713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 6.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.0628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.0587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 6.0546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.0504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.0463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.0420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 6.0378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.0335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.0290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 6.0245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.0202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 6.0113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.0069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 6.0026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.9982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 5.9938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.9894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.9851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 5.9807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.9762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.9718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.9674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 5.9630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 5.4419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3672 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 442/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 5.1209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.2306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.3138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.2160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.1356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.9775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.9188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.8645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.8215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.7915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.7601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.7557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.7311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.7237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.7166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.7092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.7033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.6989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.7032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.7076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.7163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.7182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.7187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.7179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.7161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.7128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.7090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.7057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.7016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.6987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.6966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.6942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.6908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.6870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.6829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.6785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.6741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.6691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.6641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.6595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.6548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.6506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.6469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.6393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.6354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.6313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.6273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.6231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.6186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.6139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.6097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.6057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.6021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.5989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.5925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.5891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.5857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.5823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.5790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.5753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.5718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.5685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.5616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.5586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.5557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.5526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.5495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.5464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.5434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.5404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.5372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.5341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.5313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.5285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.5257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.5229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.5204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.5177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.5124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.5097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.5069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.5040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.5011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.4983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.4955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.4927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.4900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.4873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.4845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.4817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.4799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.4760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.4740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 4.4722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.4704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 4.4672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.4658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.4645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.4630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 4.4614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.4598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.4581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 4.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 4.4527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.2348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3717 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 443/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.5215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.5553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.4529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.1624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.0546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.9813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.9124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.8479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.7963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.7622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.7327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.7122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.6828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.6670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.6545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.6428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.6174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.6036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.5899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.5850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.5585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.5563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.5559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.5564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.5569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.5571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.5569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.5567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.5565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.5565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.5571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.5576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.5578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.5579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.5578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.5577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.5572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.5566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.5555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.5550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.5536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.5530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.5522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.5513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.5503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.5488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.5606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.5719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.5931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.6219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.6310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.6397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.6483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.6566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.6648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.7005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.7068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.7130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.7194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.7255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.7540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.7688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.3188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3738 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 444/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.7219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.5196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.0564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.6998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.6938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.5265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.5244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.5224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.5011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.5005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.3316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3758 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 445/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.3681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.4084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.3890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.2754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.1990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.1578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.1217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.0868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.0510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.0035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.9708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.9680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.8250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.8237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.8224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3798 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 446/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 2.6283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.6994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.7086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.5349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.5092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.4854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.4794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.4781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.4782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.4760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.4728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.4684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4327e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.2630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3823 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 447/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.2206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.2069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.0796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.0780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.0763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.0742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.0725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.0705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3846 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 448/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.2804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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2.1390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.8893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3866 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 449/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.4991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.7096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.6994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.4197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.9447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.3418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.6398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.8706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.1703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.2616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.3293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.4144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.4765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.5228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.5608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.5862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.6050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.6214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.7877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.8577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.9627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.0023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.0330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.0833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.1031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.1301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.1479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.1608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.1706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.1766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.1787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.2111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.2223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.2359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.1894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.1779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.1129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.0988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.0840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.0051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.9885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.9717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.9545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.9371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.9196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.9018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.8838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.8657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.8476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.8295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.8113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.7934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.7754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.7574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.7210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.7029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.6305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.6124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.4885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.3845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.3674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.3334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.2995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.2827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.2660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.2493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.2327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.1997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.2415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3831 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 450/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.2132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.4800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.5379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.4388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.3392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.2610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.1914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.1296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.5809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.5648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.1680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3867 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 451/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.1611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.1428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.9589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.9633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.9571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.9488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.9476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.9463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.9451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.9442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.9435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.9425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.9415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.9392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.9381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.7867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3893 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 452/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.7028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 1.0000 - loss: 1.7302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.7441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.4012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.6935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.8148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.1268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.4754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.6243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.1782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.3388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.4861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.6210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.7462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.5831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.9064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.2080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.4961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.7681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.0274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.3848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.1853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.5551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.9030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0589e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0927e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1555e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2179e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3855e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.5075e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.5645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.3209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9065e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.5461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.7088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.9018e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.3005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.5095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7257e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.9483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.1672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.3852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.6030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.8330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.0672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.3086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.5602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.0643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.6961e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.0290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.3653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.0923e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.4821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.8870e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0298e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0727e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1156e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1604e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2091e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2604e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3131e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3670e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4244e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4829e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5434e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6058e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.0292e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9764 - val_loss: 0.3637 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 453/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9998 - loss: 6.0742e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9998 - loss: 5.9245e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9998 - loss: 5.7333e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9999 - loss: 5.4982e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9999 - loss: 5.3312e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9999 - loss: 5.2878e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9999 - loss: 5.2570e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9999 - loss: 5.2337e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9999 - loss: 5.2244e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9999 - loss: 5.2283e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9999 - loss: 5.2355e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9999 - loss: 5.2364e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9999 - loss: 5.2310e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 5.2263e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 5.2200e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9998 - loss: 5.2090e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9998 - loss: 5.1971e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9998 - loss: 5.1877e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9998 - loss: 5.1765e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9998 - loss: 5.1683e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9998 - loss: 5.1560e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9998 - loss: 5.1456e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 0.9998 - loss: 5.1386e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9998 - loss: 5.1293e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9998 - loss: 5.1214e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 0.9998 - loss: 5.1155e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9998 - loss: 5.1097e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9998 - loss: 5.1022e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9998 - loss: 5.0938e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 0.9998 - loss: 5.0850e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9999 - loss: 4.8134e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9999 - loss: 4.8061e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9999 - loss: 4.7989e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9999 - loss: 4.7920e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9999 - loss: 4.7854e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9999 - loss: 4.7786e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9999 - loss: 4.7718e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9999 - loss: 4.7651e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 4.7582e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 4.7511e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 4.7439e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 4.7365e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 4.7296e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 4.7237e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 4.7179e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 4.7122e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 4.7064e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 4.7003e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 4.6940e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 4.6877e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 4.6816e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 4.6758e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 4.6700e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 4.6639e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 4.6578e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 4.6515e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 4.6451e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 4.6390e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 4.6328e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 4.6264e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 4.6198e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 4.6132e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 4.6063e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 4.5993e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 4.5923e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 4.5852e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 4.5781e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 4.5712e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 4.5644e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 4.5575e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 4.5504e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 4.5432e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 4.5359e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 4.5285e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 4.5212e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 4.5139e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 4.5065e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 4.4990e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 4.4914e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 4.4839e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 4.4763e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 4.4687e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 4.4609e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 4.4531e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 4.4452e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 4.4373e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 4.4293e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 4.4213e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 4.4133e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 4.4053e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 4.3972e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 4.3892e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 304ms/step - accuracy: 0.9999 - loss: 3.4324e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9766 - val_loss: 0.3478 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 454/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.5658e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3948e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4547e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4459e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4042e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.3494e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3014e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2582e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2143e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1758e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1429e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1123e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0883e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0699e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0537e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0376e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0213e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0048e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8857e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.7295e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.5754e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.4457e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.3233e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.2044e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.0873e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.9750e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.8639e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.7542e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.6467e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.5405e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4360e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.3339e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.2343e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.1381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.0439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.9515e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.8615e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.7739e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.6884e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.6047e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.5227e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.4425e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.3639e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.2870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.2118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.1381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.0660e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.9953e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.9262e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.8586e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.7923e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.7274e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.6638e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.6015e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.5405e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.4806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.4219e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.3644e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.3080e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.2527e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1985e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1453e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0931e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.0420e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9918e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9425e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8942e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8467e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7542e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7092e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6649e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6215e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.5788e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.5368e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.4955e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4549e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4150e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.3758e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.3372e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2992e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2618e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2250e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1888e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1532e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.1181e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.0836e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.0496e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.0161e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.9831e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.9506e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.9186e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.8870e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8559e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.8253e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7950e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7653e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7359e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7070e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6784e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6225e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5951e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5681e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5414e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5151e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4891e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4635e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4382e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4133e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3886e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3403e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.3166e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2932e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2700e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2472e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2246e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2023e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.1803e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5599e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3789 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 455/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0801e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.8554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.7815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.7220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.6766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.6386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.5941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.5472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.5329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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9.3471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.3314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.2989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.2816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.2634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.2436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.2230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.2022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.1271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.0311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.7947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7096e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.5917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5133e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.4114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.3991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.3870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.3752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.3636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.3521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.3407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.3295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.3183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.3069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.2958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.2847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.2739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.2632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.2525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.2209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.2103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.1781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.1673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.1564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.1458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.1352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.1247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.1144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.1043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.0839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.0737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.0636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.0533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.0124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.8085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3882 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 456/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.9808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.9056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.9414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.7408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.6616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.5727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.4853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.1957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.9096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.4790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.2837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.5708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.7997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.9810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.2313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.3895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.4492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.4996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.5409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.5719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.5931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.6063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.6123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.6121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.6056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.5939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.5621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.5213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.5008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.4788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.4544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.4289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.4022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.3742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.3449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.3147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.2855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.2565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.2327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.2088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.7673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.7429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.7184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.5467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.5315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.5161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.5006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.4376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.4219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.4065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.3914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.3763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.3463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.3312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.3161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.3010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.1878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.1741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.1605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.1811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.2009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.2198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.2726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.2910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.3088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.3256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.3419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.3575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.3725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.3868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.8911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3833 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 457/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.1886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 6.5790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.6649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.4844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.3415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.1261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.0163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.9102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.8284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.7667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.7170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.6724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.6441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.6209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.5927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.5685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.5421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.5193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.4967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.4737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.4521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.4361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.4194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.4058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.3793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.3698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.3603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.3495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.3378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.1956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.1900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.1840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.1780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.1725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.1668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.1608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.1488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.1432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.0884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.0652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.3617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3911 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 458/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.0103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.0875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.1313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.0178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.0126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.8978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.8487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.8016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.7683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.7330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.7055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.6881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.6740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.6223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.6056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.5896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.5734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.5250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.5065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.5004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.4362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.4320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.4278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.3978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3128e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.2951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.2905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.2864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.2811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.2767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.2709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.0623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3960 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 459/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.0007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.2141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.1690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.1183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.0487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.7987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.7938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.7891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4002 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 460/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.3544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.6946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.6545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.6008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.4994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.3151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.3066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.2987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.2883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.2802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2739e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.0445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4038 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 461/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.0475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.9180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.8851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4064 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 462/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.9781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.0043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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1.6848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.6828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.6800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6654e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.7274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4088 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 463/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.3675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4114 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 464/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.3532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.4370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4136 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 465/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 307ms/step - accuracy: 1.0000 - loss: 1.2113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 308ms/step - accuracy: 1.0000 - loss: 1.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 34s 309ms/step - accuracy: 1.0000 - loss: 1.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 34s 311ms/step - accuracy: 1.0000 - loss: 1.2016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 34s 311ms/step - accuracy: 1.0000 - loss: 1.2005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 33s 310ms/step - accuracy: 1.0000 - loss: 1.2006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 33s 309ms/step - accuracy: 1.0000 - loss: 1.1985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 308ms/step - accuracy: 1.0000 - loss: 1.1977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 32s 307ms/step - accuracy: 1.0000 - loss: 1.1981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 32s 307ms/step - accuracy: 1.0000 - loss: 1.2017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 306ms/step - accuracy: 1.0000 - loss: 1.2032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 31s 306ms/step - accuracy: 1.0000 - loss: 1.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 31s 306ms/step - accuracy: 1.0000 - loss: 1.2053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 305ms/step - accuracy: 1.0000 - loss: 1.2046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 305ms/step - accuracy: 1.0000 - loss: 1.2029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 30s 304ms/step - accuracy: 1.0000 - loss: 1.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 304ms/step - accuracy: 1.0000 - loss: 1.1983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 304ms/step - accuracy: 1.0000 - loss: 1.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 29s 303ms/step - accuracy: 1.0000 - loss: 1.1946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 303ms/step - accuracy: 1.0000 - loss: 1.1929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 303ms/step - accuracy: 1.0000 - loss: 1.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 28s 303ms/step - accuracy: 1.0000 - loss: 1.1911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 302ms/step - accuracy: 1.0000 - loss: 1.1898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 302ms/step - accuracy: 1.0000 - loss: 1.1885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 302ms/step - accuracy: 1.0000 - loss: 1.1867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 302ms/step - accuracy: 1.0000 - loss: 1.1854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.1819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.1802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.1805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.1806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.1807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.1809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.1810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.1808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.1805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.1800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.1795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.1782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.1776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.1770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.1765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.1760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.1757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 1.1755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 1.1751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 1.1747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 1.1742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 1.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 1.1733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 1.1727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 1.1721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 1.1717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 1.1712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 1.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 1.1704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 1.1701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 1.1697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 1.1692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 1.1687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 1.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 1.1676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.1553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.1549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.1545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.1542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 1.1538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 1.1534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 1.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 1.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 1.1522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 1.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 1.1513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 1.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 1.1516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 302ms/step - accuracy: 1.0000 - loss: 1.1518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 302ms/step - accuracy: 1.0000 - loss: 1.1520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 302ms/step - accuracy: 1.0000 - loss: 1.1521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 302ms/step - accuracy: 1.0000 - loss: 1.1523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 302ms/step - accuracy: 1.0000 - loss: 1.1524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 302ms/step - accuracy: 1.0000 - loss: 1.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 302ms/step - accuracy: 1.0000 - loss: 1.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 302ms/step - accuracy: 1.0000 - loss: 1.1528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 302ms/step - accuracy: 1.0000 - loss: 1.1529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 302ms/step - accuracy: 1.0000 - loss: 1.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step - accuracy: 1.0000 - loss: 1.1531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step - accuracy: 1.0000 - loss: 1.1531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step - accuracy: 1.0000 - loss: 1.1531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step - accuracy: 1.0000 - loss: 1.1531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 306ms/step - accuracy: 1.0000 - loss: 1.1551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4153 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 466/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 310ms/step - accuracy: 1.0000 - loss: 1.2597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 303ms/step - accuracy: 1.0000 - loss: 1.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.1695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.1506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 1.0897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 1.0803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 1.0740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 302ms/step - accuracy: 1.0000 - loss: 1.0697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.0595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.0490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 303ms/step - accuracy: 1.0000 - loss: 1.0469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 303ms/step - accuracy: 1.0000 - loss: 1.0452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 29s 306ms/step - accuracy: 1.0000 - loss: 1.0432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 29s 307ms/step - accuracy: 1.0000 - loss: 1.0414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 308ms/step - accuracy: 1.0000 - loss: 1.0403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 28s 310ms/step - accuracy: 1.0000 - loss: 1.0399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 28s 311ms/step - accuracy: 1.0000 - loss: 1.0390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 28s 312ms/step - accuracy: 1.0000 - loss: 1.0379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 28s 313ms/step - accuracy: 1.0000 - loss: 1.0367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 27s 313ms/step - accuracy: 1.0000 - loss: 1.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 27s 314ms/step - accuracy: 1.0000 - loss: 1.0339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 27s 314ms/step - accuracy: 1.0000 - loss: 1.0322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 27s 315ms/step - accuracy: 1.0000 - loss: 1.0305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 26s 315ms/step - accuracy: 1.0000 - loss: 1.0291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 26s 316ms/step - accuracy: 1.0000 - loss: 1.0275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 26s 316ms/step - accuracy: 1.0000 - loss: 1.0262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 25s 317ms/step - accuracy: 1.0000 - loss: 1.0250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 25s 316ms/step - accuracy: 1.0000 - loss: 1.0242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 25s 316ms/step - accuracy: 1.0000 - loss: 1.0232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 25s 317ms/step - accuracy: 1.0000 - loss: 1.0221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 24s 318ms/step - accuracy: 1.0000 - loss: 1.0211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 24s 318ms/step - accuracy: 1.0000 - loss: 1.0199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 24s 319ms/step - accuracy: 1.0000 - loss: 1.0186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 23s 319ms/step - accuracy: 1.0000 - loss: 1.0172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 23s 319ms/step - accuracy: 1.0000 - loss: 1.0158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 23s 319ms/step - accuracy: 1.0000 - loss: 1.0146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 22s 319ms/step - accuracy: 1.0000 - loss: 1.0134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 22s 320ms/step - accuracy: 1.0000 - loss: 1.0123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 22s 320ms/step - accuracy: 1.0000 - loss: 1.0114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 22s 320ms/step - accuracy: 1.0000 - loss: 1.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 21s 320ms/step - accuracy: 1.0000 - loss: 1.0098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 21s 320ms/step - accuracy: 1.0000 - loss: 1.0091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 21s 319ms/step - accuracy: 1.0000 - loss: 1.0082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 20s 319ms/step - accuracy: 1.0000 - loss: 1.0081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 20s 319ms/step - accuracy: 1.0000 - loss: 1.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 20s 318ms/step - accuracy: 1.0000 - loss: 1.0075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 19s 318ms/step - accuracy: 1.0000 - loss: 1.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 19s 318ms/step - accuracy: 1.0000 - loss: 1.0069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 19s 318ms/step - accuracy: 1.0000 - loss: 1.0066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 18s 317ms/step - accuracy: 1.0000 - loss: 1.0063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 18s 317ms/step - accuracy: 1.0000 - loss: 1.0062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 18s 317ms/step - accuracy: 1.0000 - loss: 1.0060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 17s 317ms/step - accuracy: 1.0000 - loss: 1.0058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 17s 317ms/step - accuracy: 1.0000 - loss: 1.0056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 17s 317ms/step - accuracy: 1.0000 - loss: 1.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 16s 318ms/step - accuracy: 1.0000 - loss: 1.0052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 16s 318ms/step - accuracy: 1.0000 - loss: 1.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 16s 318ms/step - accuracy: 1.0000 - loss: 1.0047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 318ms/step - accuracy: 1.0000 - loss: 1.0044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 15s 317ms/step - accuracy: 1.0000 - loss: 1.0041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 15s 317ms/step - accuracy: 1.0000 - loss: 1.0037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 317ms/step - accuracy: 1.0000 - loss: 1.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 14s 317ms/step - accuracy: 1.0000 - loss: 1.0030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 14s 317ms/step - accuracy: 1.0000 - loss: 1.0027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 317ms/step - accuracy: 1.0000 - loss: 1.0024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 13s 317ms/step - accuracy: 1.0000 - loss: 1.0021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 13s 317ms/step - accuracy: 1.0000 - loss: 1.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 317ms/step - accuracy: 1.0000 - loss: 1.0015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 317ms/step - accuracy: 1.0000 - loss: 1.0017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 12s 317ms/step - accuracy: 1.0000 - loss: 1.0019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 12s 317ms/step - accuracy: 1.0000 - loss: 1.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 317ms/step - accuracy: 1.0000 - loss: 1.0022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 11s 316ms/step - accuracy: 1.0000 - loss: 1.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 11s 316ms/step - accuracy: 1.0000 - loss: 1.0047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 316ms/step - accuracy: 1.0000 - loss: 1.0060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 10s 316ms/step - accuracy: 1.0000 - loss: 1.0073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  88/120 ━━━━━━━━━━━━━━━━━━━━ 10s 316ms/step - accuracy: 1.0000 - loss: 1.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 315ms/step - accuracy: 1.0000 - loss: 1.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 315ms/step - accuracy: 1.0000 - loss: 1.0109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 9s 315ms/step - accuracy: 1.0000 - loss: 1.0121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 315ms/step - accuracy: 1.0000 - loss: 1.0132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 315ms/step - accuracy: 1.0000 - loss: 1.0143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 8s 315ms/step - accuracy: 1.0000 - loss: 1.0153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 314ms/step - accuracy: 1.0000 - loss: 1.0163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 314ms/step - accuracy: 1.0000 - loss: 1.0173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 7s 314ms/step - accuracy: 1.0000 - loss: 1.0182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 314ms/step - accuracy: 1.0000 - loss: 1.0191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 314ms/step - accuracy: 1.0000 - loss: 1.0200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 314ms/step - accuracy: 1.0000 - loss: 1.0209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 313ms/step - accuracy: 1.0000 - loss: 1.0218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 313ms/step - accuracy: 1.0000 - loss: 1.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 313ms/step - accuracy: 1.0000 - loss: 1.0235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 5s 313ms/step - accuracy: 1.0000 - loss: 1.0244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 313ms/step - accuracy: 1.0000 - loss: 1.0251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 313ms/step - accuracy: 1.0000 - loss: 1.0258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 4s 313ms/step - accuracy: 1.0000 - loss: 1.0265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 313ms/step - accuracy: 1.0000 - loss: 1.0272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 312ms/step - accuracy: 1.0000 - loss: 1.0279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 312ms/step - accuracy: 1.0000 - loss: 1.0286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 312ms/step - accuracy: 1.0000 - loss: 1.0293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 312ms/step - accuracy: 1.0000 - loss: 1.0299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 312ms/step - accuracy: 1.0000 - loss: 1.0305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 312ms/step - accuracy: 1.0000 - loss: 1.0310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 312ms/step - accuracy: 1.0000 - loss: 1.0316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 312ms/step - accuracy: 1.0000 - loss: 1.0322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step - accuracy: 1.0000 - loss: 1.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step - accuracy: 1.0000 - loss: 1.0333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step - accuracy: 1.0000 - loss: 1.0338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step - accuracy: 1.0000 - loss: 1.0343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 38s 315ms/step - accuracy: 1.0000 - loss: 1.0972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4183 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 467/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.1843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 1.1945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 1.1645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.1198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 1.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 1.0443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 1.0325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.0225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.0150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.0264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.0267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.0260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.0247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.0231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.0216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.0205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.0198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.0186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.0179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.0178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.0174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.0159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.0149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.0139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.0130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.0120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.0111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.0102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.0091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.0080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.0069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.0057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.0043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.0029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.0015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 9.9956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 9.9868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 9.9805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 9.9742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 9.9671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 9.9613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 9.9547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 9.9480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 9.9408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 9.9326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 9.9244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 9.9166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 9.9092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 9.9024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 9.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 9.8921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 9.8944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 9.8962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 9.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 9.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 9.8975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 9.8969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 9.8959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 9.8952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 9.8939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 9.8927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 9.8920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 9.8917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 9.8905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 9.8891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 9.8873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 9.8851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 9.8828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 1.0000 - loss: 9.8800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 1.0000 - loss: 9.8768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 1.0000 - loss: 9.8744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 1.0000 - loss: 9.8716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 1.0000 - loss: 9.8689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 1.0000 - loss: 9.8667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 9.8645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 9.8623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 9.8600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 9.8574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 1.0000 - loss: 9.8558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 1.0000 - loss: 9.8537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 1.0000 - loss: 9.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 1.0000 - loss: 9.8486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 1.0000 - loss: 9.8462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 1.0000 - loss: 9.8435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 9.8412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 9.8392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 9.8378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 9.8362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 9.8345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 9.8327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 9.8308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 9.8286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 9.8261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 9.8236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 9.8218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 9.8199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 9.8180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 9.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 1.0000 - loss: 9.8153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 1.0000 - loss: 9.8137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 1.0000 - loss: 9.8125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 1.0000 - loss: 9.8113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 1.0000 - loss: 9.8098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 1.0000 - loss: 9.8082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 9.8064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 9.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 9.8026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 9.8048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 1.0000 - loss: 1.0075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4192 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 468/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 304ms/step - accuracy: 1.0000 - loss: 1.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.9945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.9668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.9532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.9392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.9094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.8951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.8238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.8108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.7977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.7845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.7714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.7579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.7449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.7326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.7199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.7076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.5955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.5768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.5501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.5411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.5320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.5230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.5138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.5051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.4969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.4889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.4809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.4732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.4656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.4271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.4193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.4117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.4044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.3639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.3441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.5331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4212 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 469/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.0323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.9105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.6697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.4728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.2998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.1957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.0614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 8.9576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 8.8854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 8.8083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 8.7518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 8.7095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 8.6740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 8.6367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 8.6090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 8.5830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - 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8.3572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 8.3476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 8.3360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 8.3239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 8.3101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 8.2958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 8.2802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 8.2634e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.8252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.8204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.8165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.8135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.8112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.8086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.8059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.8031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.8003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.7912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.7882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.7850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.7738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.7714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.7698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.7680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.7660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.7640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.7623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.7605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.7590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.7575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.7566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.7555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.7543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.7530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.7516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.7502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 7.7486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 7.7471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 7.7456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 7.7440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 1.0000 - loss: 7.5496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4233 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 470/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.0624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.3299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.3566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.3366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.3178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.3012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.2858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.2713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.9882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.9571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.9268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.8971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.8678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.8395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.8127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.7859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.6838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.6595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.6357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.6126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.5896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.5670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.5454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.4858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.4669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.4482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.4295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.4109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.3926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.3750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.3575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.3237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.3072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.2906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.2742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.2579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.2418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.2259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.2100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.1791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.1638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.1490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.1345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.1204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.1063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.0923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.0782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.0643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.0505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.0367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.0232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.9970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 304ms/step - accuracy: 1.0000 - loss: 7.4533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4252 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 471/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 8.2156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 304ms/step - accuracy: 1.0000 - loss: 8.2572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.3368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.1209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.3023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.3272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.2774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 8.1919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 8.1010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 8.0234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.9829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.9342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.8943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.8683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.8505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.8199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.5746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.5442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.5235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.5063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.4729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.4549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.4377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.4193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.3993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.3788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.3594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.3422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.3109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.2981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.2864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.1932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.2349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.2731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.3089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.3424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.3742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.4035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.4309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.4568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.4806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.5020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.5212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.5390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.5559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.5715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.5867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.6278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.6497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.6592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.6676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.6747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.6817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.6884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.6943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.6997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.7047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.7095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.7135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.7173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.7205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.7233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.7254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.7271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.7287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.7301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.7311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.7318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.7328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.7336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.7340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.7341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.7339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.7302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.7290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.7276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.7208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.7187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.7141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.7115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.7087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.7058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.7030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.7000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.6971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.6942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.6912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.6882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.6874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.6864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.6853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.6839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 304ms/step - accuracy: 1.0000 - loss: 7.5186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4264 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 472/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.5109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 6.7091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.7087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.5339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.4605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.4668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.4516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.3909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.3910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.3568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.3385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.2528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.6481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.9959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.3056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.5811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.2441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.4171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.7047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.8237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.9325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 1.0000 - loss: 1.0437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 1.0278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 1.0267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 304ms/step - accuracy: 1.0000 - loss: 8.8292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4267 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 473/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.0828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.9925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.7584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.4442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.3363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.2292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.0656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.9917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.9726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.9609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.9565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.9485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.9468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.9432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.9449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.9427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.9370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.9359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.9376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.9021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.9060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.9094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.9133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.9218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.9557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.9590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.9614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.9649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.9659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.9663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.9488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.9481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.9473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.9456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.9434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.9428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.9422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.9415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.9407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.9398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.9390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.9382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 5.8407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4283 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 474/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.7670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.5893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.5058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.2495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.0201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.8315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.6706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.4081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.4528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.4399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.4237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.3379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.3133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.2112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.0094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.7150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.6967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.6786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.6608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.6262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.6094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.5928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.5767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.5609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.5454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.5301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.5152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.5006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.4861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.4719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.4580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.4443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.4309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.4177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.3202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.3089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.2979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.2869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.2762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.2656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.2348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.2248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.2149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.2052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.1956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.1862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.1769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.1677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.1586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.1410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.1323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.1237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.1153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.1069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 304ms/step - accuracy: 1.0000 - loss: 1.1710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4277 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 475/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 306ms/step - accuracy: 1.0000 - loss: 5.6527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.1713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.3765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.3149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.2673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.2364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.1905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.0955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.1065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.1162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.1356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.1488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.1681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.1666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.1679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.1993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.2160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.2192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.2232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.2307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.2343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.2366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.2394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.2408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.2417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.2419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.2418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.2411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.2399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.2390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.2394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.2398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.2403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.2408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.2544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.2571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.2590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.2628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.2646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.2662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.2699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.2714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.2729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.2744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.2758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.2770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.2778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.2798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.2813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.2822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.2849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.2813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.2811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.2807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 6.2050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4287 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 476/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.8445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.5266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.4282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.0156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.8755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.6977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.6192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.5947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.6019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.6034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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5.8226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.8919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.9206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.0131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.1223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.1276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.1297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.1311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.1330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.1368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.1408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.1435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.1478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.1496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.1505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.1512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.1516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.1520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.1562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.1595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.1630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.1856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.1871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.1885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.1906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.1910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.1906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.1896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.1842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.1834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.1673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.1659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.1643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.9731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4301 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 477/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.9957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.1742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.1301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.9602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.8729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.7876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.6658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.5241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.4920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.4416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.4007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.3670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.3644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.3625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.3644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.3758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.3828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.3988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.3997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.4326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.4462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.4572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.4676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.4774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.4861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.4932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.4988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.5038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.5090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.5128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.5165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.5200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.5236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.5259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.5308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.5312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.5315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.5323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.5326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.5326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.5324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.5318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.5310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.5296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.5280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.5265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.5260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.5256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.5252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.5248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.5241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.5232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.5219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.5208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.5195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.5178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.1343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4323 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 478/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 5.5750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.4165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.3867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.2552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.1373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.0608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.0211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.9666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.9154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.8781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.8482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.8871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.8962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.9019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.9085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.9126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.9193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.9250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.9301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.9320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.9319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.9510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.9515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.9518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.9525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.9542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.9561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.9684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.9699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.9707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.9708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.9712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.9743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.9745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.9742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.9739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.9732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.9726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.9598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.9589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.9581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.9573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.9564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.9555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.9545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.9540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.9533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.9523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.9514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.9506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.9498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.9483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.9477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.7599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4342 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 479/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.7089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.8397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.9468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.7740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.7400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.7163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.6863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.6545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.6205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.6078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.5876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.5736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.5470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.5510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.5560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.5598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.5622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.5628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.5621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.5601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.5563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.5530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.5506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.5476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.5447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.5421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.5393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.5361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.5335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.5304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.5122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.5075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.5028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.4642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.4605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.4569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.4543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.4518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.4494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.4471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.4448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.4425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.4403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.4378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.3709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.3699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.2866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4356 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 480/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.4220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.6587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.9652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.9269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.7101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.6637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.5929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.5089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.4264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.3611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.3104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.2440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.2247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.2093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.1998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.1864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.1731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.0935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.0750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.0584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.0423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.0288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.0149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.8977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.8837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.8712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.8606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.8494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.8387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.8278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.8176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.8070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.7966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.7862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.7761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.7661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.7567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.7244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.7090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.6501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.6442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.6382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.6322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.6259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.6199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.6138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.6079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.6018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.5959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.5900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.5844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.5786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.5728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.5672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.5616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.5571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.5524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.5477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.5438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.5398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.5361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.5331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.5302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.5270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.5238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.5206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.5173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.5138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.5102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.5066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.5031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.4995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.4960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.4926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.4893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.4721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.4686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.4651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.4619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.4552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.4399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.4338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.0794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4375 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 481/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.8376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.8447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.8302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.7316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.7149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.7154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.7100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.7157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.7249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.7333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.7314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.7267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.6958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4393 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 482/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.4404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.2494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.4555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.3840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.2325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.1650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.1287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.0908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.0129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.9911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.9988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.9851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.9812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.9801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.9785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.9767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.9755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.9792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.9814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.9829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.9841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.9853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.9859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.9857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.9850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.9281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.9189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.9164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.9140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.9116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.9093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.9072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.9051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.9028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.9009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.0005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4405 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 483/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.8802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.7555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.5549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.3997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.2837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.2305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.2174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.2099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.2068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.2017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.1957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.1905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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3.1856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.1890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.1915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1904e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.2373e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.5341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4421 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 484/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 3.4689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.2868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 36s 316ms/step - accuracy: 1.0000 - loss: 3.1818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 36s 313ms/step - accuracy: 1.0000 - loss: 3.0569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 35s 311ms/step - accuracy: 1.0000 - loss: 2.9893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 35s 310ms/step - accuracy: 1.0000 - loss: 2.9517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 35s 310ms/step - accuracy: 1.0000 - loss: 2.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 34s 309ms/step - accuracy: 1.0000 - loss: 2.9050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 34s 307ms/step - accuracy: 1.0000 - loss: 2.8881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 306ms/step - accuracy: 1.0000 - loss: 2.8727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 33s 305ms/step - accuracy: 1.0000 - loss: 2.8664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 305ms/step - accuracy: 1.0000 - loss: 2.8666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 304ms/step - accuracy: 1.0000 - loss: 2.8700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 32s 303ms/step - accuracy: 1.0000 - loss: 2.8734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 303ms/step - accuracy: 1.0000 - loss: 2.8868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 303ms/step - accuracy: 1.0000 - loss: 2.8942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 31s 302ms/step - accuracy: 1.0000 - loss: 2.9015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 302ms/step - accuracy: 1.0000 - loss: 2.9096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 302ms/step - accuracy: 1.0000 - loss: 2.9149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 302ms/step - accuracy: 1.0000 - loss: 2.9174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 302ms/step - accuracy: 1.0000 - loss: 2.9166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 302ms/step - accuracy: 1.0000 - loss: 2.9164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 302ms/step - accuracy: 1.0000 - loss: 2.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 302ms/step - accuracy: 1.0000 - loss: 2.9255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 302ms/step - accuracy: 1.0000 - loss: 2.9339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 302ms/step - accuracy: 1.0000 - loss: 2.9439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 28s 302ms/step - accuracy: 1.0000 - loss: 2.9544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 2.9623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 2.9690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 2.9747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 2.9790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 2.9824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 2.9845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 2.9859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 2.9878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 2.9906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 2.9946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 2.9984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 3.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 3.0052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 3.0078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 3.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 3.0124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 3.0141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 3.0154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 3.0166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 3.0184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 3.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 3.0226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 3.0246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 3.0268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 3.0284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 3.0297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.0307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.0316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.0322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.0326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.0330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 3.0331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 3.0332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 3.0337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 3.0345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 3.0351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 3.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 3.0357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 3.0357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 3.0360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 3.0360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 3.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 3.0360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.0358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.0351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.0346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.0344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.0341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.0337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.0334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.0331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.0324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.0327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.0330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.0338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.0343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.0347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.0351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.0362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.0366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.0368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.0371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.0374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.0377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.0384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.0387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.0389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.0391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.0399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.0402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.0405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.0408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.0745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4438 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 485/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.2310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.1806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.1934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.1217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.0602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.9651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.8719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.8724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.8721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.8800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.8824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.8838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.9215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.9213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.9209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.9210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.9209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.9207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.9204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.9200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.9195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.9192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.9188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.9183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.9179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.9174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.9160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.9154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.9147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.9140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.9132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.9119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.9112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.9106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.9106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.9105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.9104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.9104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.9103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.9102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.9101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.9098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.9095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.9093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.9092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.9090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.9089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.9087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.1250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.5408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.9354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.1277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.6868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.7053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4434 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 486/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.6590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 3.4425e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.1946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 4.8295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 5.4514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 6.1388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.9798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.4664e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0037e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.1678e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.4414e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.7816e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 2.2187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 2.6913e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 3.2264e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 3.8613e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 4.5725e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 5.3955e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 6.2323e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 7.1076e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 8.0520e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 9.0455e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.0171e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.1367e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.2606e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.3903e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.5294e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.6725e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.8151e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 0.9999 - loss: 1.9594e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 0.9999 - loss: 4.5730e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9999 - loss: 4.6565e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9999 - loss: 4.7418e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 0.9999 - loss: 4.8239e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9999 - loss: 4.9046e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9999 - loss: 4.9857e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 0.9998 - loss: 5.0644e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9998 - loss: 5.6530e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9998 - loss: 5.7205e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9998 - loss: 5.7861e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 0.9998 - loss: 5.8497e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9998 - loss: 5.9117e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9998 - loss: 5.9719e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 0.9998 - loss: 6.0304e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9998 - loss: 6.0870e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9998 - loss: 6.1426e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 0.9998 - loss: 6.1966e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9998 - loss: 6.2493e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9998 - loss: 6.2999e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9998 - loss: 6.3489e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 0.9998 - loss: 6.3966e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9998 - loss: 6.4426e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9998 - loss: 6.4871e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 0.9998 - loss: 6.5302e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9998 - loss: 6.5718e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9998 - loss: 6.6125e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 0.9998 - loss: 6.6519e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9998 - loss: 6.6901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9998 - loss: 6.7269e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9998 - loss: 6.7623e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 0.9998 - loss: 6.7963e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9998 - loss: 6.8290e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9998 - loss: 6.8605e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 0.9998 - loss: 6.8911e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9998 - loss: 6.9208e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9998 - loss: 6.9491e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 0.9998 - loss: 6.9763e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9998 - loss: 7.0023e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9998 - loss: 7.0274e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9998 - loss: 7.0513e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 0.9998 - loss: 7.0744e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9998 - loss: 7.0966e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9998 - loss: 7.1177e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 0.9998 - loss: 7.1378e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9998 - loss: 7.1570e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9998 - loss: 7.1754e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 0.9998 - loss: 7.1928e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9998 - loss: 7.2094e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9998 - loss: 7.2251e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9998 - loss: 7.2400e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.9998 - loss: 7.2543e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 0.9997 - loss: 8.9451e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998 - val_accuracy: 0.9763 - val_loss: 0.3173 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4986 Epoch 487/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3284e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.3476e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2674e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1874e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1275e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0842e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0468e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0103e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.7570e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1723e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.9816e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7982e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.6184e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.4464e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.2777e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.1137e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.9544e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8017e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6539e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.5118e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3760e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.2463e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.1252e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.0099e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.8997e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.7936e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.6909e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.5916e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.4957e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.4029e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.3131e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2260e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.1415e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0598e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9804e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9037e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 5.8293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 5.7571e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 5.6869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 5.6187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 5.5524e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 5.4878e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 5.4249e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 5.3638e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 5.3044e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 5.2465e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 5.1901e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 5.1351e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 5.0815e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 5.0293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 4.9783e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 4.9285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 4.8799e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 4.8325e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 4.7861e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 4.7407e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 4.6964e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 4.6530e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 4.6106e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 4.5691e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 4.5285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 4.4887e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 4.4498e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 4.4117e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 4.3743e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 4.3377e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 4.3018e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 4.2666e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 4.2321e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 4.1982e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 4.1650e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 4.1324e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 4.1005e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 4.0691e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 4.0383e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 4.0080e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 3.9783e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 3.9491e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 3.9204e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 1.0000 - loss: 3.8922e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 1.0000 - loss: 3.8645e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 301ms/step - accuracy: 1.0000 - loss: 3.8373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 1.0000 - loss: 3.8105e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 1.0000 - loss: 3.7841e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 301ms/step - accuracy: 1.0000 - loss: 3.7582e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 3.7327e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 3.7077e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 3.6830e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 301ms/step - accuracy: 1.0000 - loss: 3.6587e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 1.0000 - loss: 3.6348e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 1.0000 - loss: 3.6113e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 301ms/step - accuracy: 1.0000 - loss: 3.5881e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 1.0000 - loss: 3.5653e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 1.0000 - loss: 3.5428e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 301ms/step - accuracy: 1.0000 - loss: 3.5206e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 3.4988e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 3.4774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 3.4562e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 301ms/step - accuracy: 1.0000 - loss: 3.4354e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 3.4148e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 3.3945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 301ms/step - accuracy: 1.0000 - loss: 3.3745e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 3.3549e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 3.3356e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 301ms/step - accuracy: 1.0000 - loss: 3.3165e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 3.2977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 3.2792e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 3.2609e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 301ms/step - accuracy: 1.0000 - loss: 3.2428e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 1.0000 - loss: 3.2250e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 1.0000 - loss: 3.2074e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 301ms/step - accuracy: 1.0000 - loss: 3.1901e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 1.0000 - loss: 3.1729e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 1.0000 - loss: 3.1560e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 301ms/step - accuracy: 1.0000 - loss: 3.1393e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 3.1229e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 3.1066e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 3.0905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 1.0000 - loss: 3.0746e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 37s 305ms/step - accuracy: 1.0000 - loss: 1.1847e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3359 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 488/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 2.1827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.1541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 2.1338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.1122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.0878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 2.0634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 2.0494e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 2.0359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 2.0272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 2.0172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 2.0070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 2.0001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.9946e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.9864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.9793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.9718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9468e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9392e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9328e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9131e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8718e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8347e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8281e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8187e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8156e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8125e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.7940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.7827e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7800e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.7746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.7719e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.7692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.7665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7637e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7537e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7490e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7346e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7206e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7183e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7161e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7069e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7047e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7003e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6981e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6852e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6830e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6787e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6643e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6583e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3450 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 489/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1632e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2365e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2067e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4237e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4990e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5881e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6601e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6660e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6557e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6473e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6233e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5903e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5756e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5708e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5610e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5466e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5330e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5287e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5247e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5163e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5080e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4662e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4594e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4309e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4218e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4188e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4159e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4130e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3966e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3940e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3914e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3811e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3737e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3689e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3665e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3642e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3506e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3380e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3321e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.1016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3501 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 490/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.9209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.2257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.3699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.1190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.9579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.7613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.7102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.7091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.6960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.6813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.6632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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8.5153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.5054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.4922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.4802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.4239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.4133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.4029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.3942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.3874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.3817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.3743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.3668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.3589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.3503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.3416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.3325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.3235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.3162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.3084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.3008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.2435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.2278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.1981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.1686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.1611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.1533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.9970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.9841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.9779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.9717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.9657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.9599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.9543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.8958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.8449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.8335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3552 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 491/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.9744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.0013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.8513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 7.5585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.3385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.1694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.0418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.9406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.7538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.3565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.3393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.3216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.3067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.2945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.2422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.2027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.1950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.1916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.1104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.1021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.0992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.0980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.0966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.7043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3598 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 492/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.6055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.7681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.7336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.7108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.6924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.6814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.6787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.6663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.6531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.6023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.5665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.5052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.4938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.4808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.4561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.4437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.4312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.3006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.1944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.0934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.0776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.0425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.0288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.0099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.0052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.0007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.4938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3637 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 493/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.0125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.1787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.1956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.0902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.9741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.8653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.6790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.6098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.4872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.4654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.4506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.4323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.3392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.3218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.3070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.2927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.2796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.2683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.2584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.2473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.2370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.1927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.1866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.0301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.0281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.0259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3672 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 494/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.5676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.4190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.3795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.8542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.7423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.7438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.7433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.7411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.7402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.7615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.7715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.7806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.7928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.7955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.7940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.7926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.7823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.6999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.6928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.6892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.6186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.6164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.6142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.6096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.6073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.6050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.6026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.6002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.3176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3702 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 495/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.5914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.5398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.4581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.4223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.2894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.2757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.2633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.2248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.2184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.2143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.2153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.2137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.2118e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.1450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.1425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.1375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.0998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.7245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.8721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.8996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.9264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.9523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.9774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.0022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.1570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.1766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.1956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.2296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3707 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 496/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.0315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.5796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.5208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.5153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.5128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.5116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.5088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.5059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.5020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.4937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.4813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.4778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.4744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.4712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.3663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.3648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.3633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.3328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3724 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 497/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.4174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.5407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.3826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.3014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.1570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.0850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.0220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.9036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.8299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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3.6846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.6737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.6619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.6503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.6390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.6279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.6167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6058e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4319e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3830e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.0010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3743 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 498/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.6713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.0509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.8599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.8521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.8453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.8396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.8340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.7937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.7855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.7799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.7558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.7529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.7495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.6251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3770 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 499/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 2.9506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.5218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.5036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.3270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.3171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.2996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.2976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.2956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.2924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.2802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.2787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 2.2773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 2.2727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.2716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.2704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 2.2691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.2678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.2664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 2.2650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.2637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.2623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.2610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 2.2597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.2572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 2.2559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.2546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.2533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.2520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 2.2506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.2479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 2.2466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.2453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.2429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.2370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.2357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.2321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 2.2310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 2.2269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.2259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.2248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 2.2238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.2206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 2.2195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 2.0912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3794 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 500/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.2117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.2542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.2169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.1827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.1128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.0842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.0661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.0560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.0438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.0345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.0284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.0158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.0125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.0086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.0035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.9987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.9952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.9911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.9892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.9881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.9870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.9855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.9850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.9841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.9828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.9812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.9793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.9772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.9756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.9737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.9724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.9717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.9682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.9653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.9615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.9601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.9578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.9510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.9479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.9167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.9150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.9134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.9115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.9109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.8320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3819 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 501/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.8247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.8222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.8173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.8151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.8128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3842 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 502/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.6390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.6778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.7752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.6879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3862 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 503/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3881 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 504/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.7973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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1.9496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3898 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 505/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 1.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.3106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.3082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.3057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.2986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 1.2800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 1.2687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 1.2609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 1.2552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 1.2483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 1.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.2417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.2404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.2385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.2376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.2336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.2342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.2362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.2404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.2405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.2404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.2405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.2425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.2429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.2433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.2437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.2446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.2450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.2457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.2466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.2468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.2470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.2474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.2476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.2484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.2485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.2485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.2517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3916 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 506/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.1002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.1002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.0937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3935 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 507/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.3532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.0498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3954 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 508/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.8517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.8152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.7921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.7580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.7414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.7236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.6989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.6781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.6572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.6366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.6235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.6110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.6046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.5996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.5996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.5945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.5912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.5867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.5804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.5732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.5640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.5552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.5496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.5440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.5388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.5343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.5320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.5273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.5224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.5164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.5104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.5043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.4977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.4910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.4862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.4816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.4783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.4766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.4759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.4744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.4724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.4701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.4673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.4635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.4589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.4541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.4504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.4456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.4416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.4383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.4289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.4276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.4262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.4247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.4238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.4232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.4221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.4208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.4199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.4187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.4173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.4158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.4143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.4130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.4114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.4100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.4091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.4086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.4078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.4069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.4059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.4046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.4034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.4003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.3595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.3570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.3544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3973 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 509/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 9.6146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.7951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 1.0008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 9.8195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 9.6521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 9.5046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 9.3610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.2432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.0790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.0113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.9715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.9374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.9154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.9291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.9369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.9378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.9332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.9746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.9778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.9823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.9442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.9435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.9396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.9252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.9249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.9255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.9249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.9242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.9231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.9217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.9003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.6649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3991 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 510/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.1915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.6908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.1208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.1442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.2062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.1826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.1153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.0293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.9463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.5981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.5696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.5399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.5165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.4693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.3833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.3658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.3478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.3288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.2972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.2829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.2697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.2385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.2304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.2213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.2122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.2026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.1828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.1744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.1662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.1583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.1506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.1435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.1356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.0909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.0855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.0764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.0730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.0701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.0602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.0554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.0464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.0078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.0052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.0029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.0006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.9984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.9589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.9564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.9541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.6645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4008 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 511/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 7.5492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.5972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.7820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.7066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.6278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.5470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.5176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.4746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 7.4203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.3749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.3509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.3270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.3190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.3186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.3274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.3275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.3278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.3269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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7.2918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.2932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.2934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.2933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.2951e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.4880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.4892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.4907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.4947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.5010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.5020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.5028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.5035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.5046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.5055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.5968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4026 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 512/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.8718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.1599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.1780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.9044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.6551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.4336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.2274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.1643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.1185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.1165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.1118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.1084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.0338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.0322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.0379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.0450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.0526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.0605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.0770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.0923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.1024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.1088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.1135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.1148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.1147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.1114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.1060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.1004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.0934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.0863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.0800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.0749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.0609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.0445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.0370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.0283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.0109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.0018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.9930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.9786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.9713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.9639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.9561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.9479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.9400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.9314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.9315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.9314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.9306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.9298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.9294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.9288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.9274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.9256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.9233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.9208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.9179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.9151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.9123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.9097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.9068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.9039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.9011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.8986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.8955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.8922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.8886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.8852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.8815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.8774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.8734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.8697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.8659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.8620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.8584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.8489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.8338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.8219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.8182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.8113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.8077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.8040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.8002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.2220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4041 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 513/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.5391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.3753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.5751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.0068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.8368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.7200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.6142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.5154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.3674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.3020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.1021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.0321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.0141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.0018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.9126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.9191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.9307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.9517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.9540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.9552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.9560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.9560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.9553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.9542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.9534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.9520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.9513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.9500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.9489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.9479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.9471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.9460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.9445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.9430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.9416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.9413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.9407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.9401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.9395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.9393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.9077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.9065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.9053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.9040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.9027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.9013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.7314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4058 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 514/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.0200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.5556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.3694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 8.0163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 7.7181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 7.5238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.3631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.2241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.1123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.9502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.8877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.8473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.7685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.7470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.7249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.7019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.6779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.6543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.6360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.6165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.5848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.5732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.5597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.5464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.4959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.4723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.4423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.3768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.3561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.3499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.3437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.3386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.3235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.2071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.2055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.2049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.1927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.1926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.1927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.1920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.1707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4071 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 515/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.0584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.0737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.2002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.9586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.8748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.7931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.7154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.4989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.4335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.3690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.3077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.2490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.1378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.0849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.9836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.9348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.8869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.8413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.7974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.7545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.7129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.6735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.6359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.5985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.5620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.5261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.4914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.4579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.4246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.3920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.3604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.3291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.2988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.2696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.2416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.1371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.1137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.0907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.9824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.9628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.9433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.9247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.9063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.8882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.8020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.7854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.7695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.7539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.7383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.7229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.7078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.4874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.3939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.3722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.1050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4091 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 516/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 8.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 8.4135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.7182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.4499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.2429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.0466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.8746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.5917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.5175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.2986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.2741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.2459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.2182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.1640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.1417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.0832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.8916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.8826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.8732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.8633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.8534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.8454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.8379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.8307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8185e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.7756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.7702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.7649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.7599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.7550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.7500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.7452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.7404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.7361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.7318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.7272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.7227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.7185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.7141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.7100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.7060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.7024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.6984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.6945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.6905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.6864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.6822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.6779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.6744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.6713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.6681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.6650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.6622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.6541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.6255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.6256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.6256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.6255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.6007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4108 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 517/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.7675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.3167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.6288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.7828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.8372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.8352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.7886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.7347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.6703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.5749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.5400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.5075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.4880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.4722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.4551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.4426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.4296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.4198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.4091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.3967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.3845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.3730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.3614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.3511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.3428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.3383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.3317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.3174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.3108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.3032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.2955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.2875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.2803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.2725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.2662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.2622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.2597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.2567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.2538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.2505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.2468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.2430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.2671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.2893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.3102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.3296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.3485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.3665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.3848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.4012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.4165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.4307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.4974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.5066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.5154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.5236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.5310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.5381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.5446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.5510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.5569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.5623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.5673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.5720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.5761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.6009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.6029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.6047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.6063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.6076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.6093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.6109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.6125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.6139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.6153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.6166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.6177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.6185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.6192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.6197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.6203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.6206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.6208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.6211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.6214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.6210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.6209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.6207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.6204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.6200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.6195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.6189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.6171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.6148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.6108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.4834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4112 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 518/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.2285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.3367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.6758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.7093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.7143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.7176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.7974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.9047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.9582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.0328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.0950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.1323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.1638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.1956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.2255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.2421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.2523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.2579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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5.2175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.2142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 5.2084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.2026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.1956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.1885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.1812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.1742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.1661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.1598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.1467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.1421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.1403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.1375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.1347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 5.1316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.1282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.1243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 5.1197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 5.1060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.1053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.1048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 5.1035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.1019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.0997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.0974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.0860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.0828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.0795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.0774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.0711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 5.0599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.0569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.0541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.0511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.0483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.9985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.9963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.7854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4130 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 519/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 6.7741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 6.5543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.4517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.9390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.7385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.5940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.4771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.0515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.0235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.9971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.9258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.7928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.7808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.7679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.7555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.7436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.7362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.7230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.7163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.7110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.7048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.7012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.6984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.6967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.6938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.6905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.6868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.6830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.6790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.6750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.6709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.6674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.6643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.6621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.6604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.6591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.6573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.6553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.6533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.6511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.6485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.6422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.6444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.6449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.6452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.6452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.6449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.6442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.6434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.6416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.6408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.6400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.6388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.6382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.6378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.6373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.6369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.6365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.6360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.6355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.6352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.6347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.6342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.6338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.6335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.6330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.6325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.6254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.6248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.6241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.6234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.6226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.6217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.6208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.6200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.6191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.6183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.6175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.6169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.6161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.6153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.6144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.6134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.6124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.6124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.6123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.6122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.6121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.5904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4146 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 520/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.1059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.7848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.5602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.2909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.1799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.0952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.0127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.9417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.7909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.7567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.7371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.7161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.6946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.6727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.6519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.6177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.6015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.5862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.5744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.4992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.4818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.4740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.4673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.4622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.4087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.4060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.4049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.4030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.4011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.3991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.3967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.3944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.3925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.3904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.3888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.3871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.3855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.3839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.3827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.3812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.3797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.3583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.3524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.3442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.3421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.3402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.3384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.3331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.3170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.3113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.1918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4161 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 521/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.0302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.0699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.2617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.4127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.4015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.2906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.2930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.2916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.2855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.2941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.2999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.3031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.3078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.3106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.3156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.3008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.2993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.2975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.2954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.2879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.2810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.2764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.2763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.2757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.2759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.2756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.2751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.2758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.2811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.2817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.2821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.2824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.2825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.2823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.2838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.2853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.2861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.2866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.2870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.2871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.2869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.2866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.2863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.2862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.2860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.2857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.2854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.2849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.2843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.2828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.2820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.1387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4178 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 522/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.3267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.2278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.1544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.0478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.9475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.8684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 4.6869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.6070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.5777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.5512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.5195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.4001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.3488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.3279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.3062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.9086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.5299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.0930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.6029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.4893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.5546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.8559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.4115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.6602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.8896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.6611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.2366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2231e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9441e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3504e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.9205e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.0396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3558e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0793e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2379e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4089e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8631e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1199e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4007e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7078e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0305e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3598e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.7068e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.0642e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4279e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.7940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1687e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.5579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.9550e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.3542e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.7615e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.1781e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5956e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.0100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.4253e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8381e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.2439e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6446e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0042e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0434e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0821e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1201e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1577e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1948e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2312e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2668e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3019e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3365e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3704e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4036e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4363e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4683e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4996e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5303e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 0.9998 - loss: 5.1796e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9763 - val_loss: 0.3375 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4988 Epoch 523/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 0.9999 - loss: 2.6335e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9999 - loss: 3.0021e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9999 - loss: 3.0932e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9999 - loss: 3.0021e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9999 - loss: 2.9255e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 0.9999 - loss: 2.8640e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9999 - loss: 2.8004e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9999 - loss: 2.7421e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9999 - loss: 2.6861e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9999 - loss: 2.6368e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9999 - loss: 2.5964e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9999 - loss: 2.5550e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 0.9999 - loss: 2.5144e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9999 - loss: 2.4780e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 0.9999 - loss: 2.4460e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9999 - loss: 2.4118e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9999 - loss: 2.3776e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9999 - loss: 2.3463e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9999 - loss: 2.3157e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9999 - loss: 2.2883e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9999 - loss: 2.2607e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9999 - loss: 2.2328e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9999 - loss: 2.2053e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9999 - loss: 2.1783e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9999 - loss: 2.1521e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9999 - loss: 2.1267e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9999 - loss: 2.1024e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9999 - loss: 2.0782e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9999 - loss: 2.0545e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 0.9999 - loss: 2.0319e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9999 - loss: 1.8686e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 0.9999 - loss: 1.8502e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9999 - loss: 1.8321e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9999 - loss: 1.8144e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9999 - loss: 1.7970e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 0.9999 - loss: 1.7800e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9999 - loss: 1.7633e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9999 - loss: 1.7469e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 0.9999 - loss: 1.7308e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9999 - loss: 1.7149e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9999 - loss: 1.6994e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 0.9999 - loss: 1.6841e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9999 - loss: 1.6691e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9999 - loss: 1.6544e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9999 - loss: 1.6401e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9999 - loss: 1.6260e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9999 - loss: 1.6121e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9999 - loss: 1.5985e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9999 - loss: 1.5852e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9999 - loss: 1.5721e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9999 - loss: 1.5592e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9999 - loss: 1.5465e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5341e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5218e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5098e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4980e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4864e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4750e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4637e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4527e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4418e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4311e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4206e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4103e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4001e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3802e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3705e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3609e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3515e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3423e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3331e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3242e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3153e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3066e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2980e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2895e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2811e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2729e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2648e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2568e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2489e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2412e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2335e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2259e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2185e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2111e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2039e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1967e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1897e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1827e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1758e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1690e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1623e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1557e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1492e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1427e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1364e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1301e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1239e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1177e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1057e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0998e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0940e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0882e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0825e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0769e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0713e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0658e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0604e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0550e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0497e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.1772e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.3436 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 524/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0314e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.0748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1026e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0699e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0384e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9999e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9077e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.8987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.8925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.8860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8784e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8621e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8549e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8475e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8366e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8272e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8115e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.8002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7947e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7894e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7840e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7791e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7744e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7563e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7379e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7336e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7252e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7135e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6938e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6901e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6693e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6626e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6560e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6527e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6432e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6400e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6339e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6277e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6246e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6093e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6063e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6034e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5977e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5919e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5891e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5862e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5834e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5778e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5751e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5724e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5644e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5566e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5514e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5489e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5438e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5174e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5150e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5127e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2354e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3512 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 525/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2424e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.1645e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.1361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2385e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2816e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2847e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2825e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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1.2487e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2403e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2177e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1933e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1895e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1552e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1357e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1171e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1038e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1012e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0985e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0884e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0722e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0700e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0677e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0655e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0633e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0510e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0491e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0471e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0452e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0433e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0414e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0395e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0376e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0340e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0304e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0286e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0251e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0165e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0132e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0100e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0068e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0036e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0021e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0005e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.9895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3581 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 526/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.6318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.8581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.6416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.4944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.3645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.2362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.1208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 7.0216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.9559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.8943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.8439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.7831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.7524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.6875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.6542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.6238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.5925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.5635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.5396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.5148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.4919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.4710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.4526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.4327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.4140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 6.3960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 6.3786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 6.3613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.2963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.2823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.2703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.2602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.2498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.2285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.2178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.2071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.1962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.1859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.1766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.1671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.1500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.1356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.1289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.1217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.1148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.1077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.0868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.0740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.0682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.0632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.0579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.0523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.0467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.0411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.0296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.0237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.0183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.0128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.0025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.9976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.9925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.9872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.9818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.9766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.9715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.9662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.9611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.9562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.9516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.9472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.9431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.9391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.9351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.9310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.9268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.9227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.9184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.9141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.9098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.9057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.9015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.8896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.8732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.8606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.8484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.8044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3631 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 527/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.3765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.4572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.4338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.2659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.0172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.8877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.8310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.8336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.8307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.8256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.7899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.7844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.7799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.7764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.7717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.7657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.7590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.7520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.7446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.7365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.7231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.7165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.7115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.7075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.7043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.7001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.6959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.6918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.6828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.6782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.6738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.6621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.6590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.6563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.6530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.6495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.6457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.6418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.6378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.6335e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.5756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.5720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.5661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.5632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.5602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.5572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.5541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.5509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.5476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.5444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.5414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.5384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.5355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.5329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.5305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.5281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.5256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.5230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.5205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.5179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.5124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.5099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.5072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.5046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.1649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3674 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 528/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 307ms/step - accuracy: 1.0000 - loss: 4.6009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.6400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.6642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.0897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.0351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.8555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.8414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.8207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.8122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.7977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.7777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.7755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.7650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.7592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.7547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.6453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.4823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3708 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 529/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 3.0427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.5564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.7363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.7012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.6464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.5474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.4788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.4553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.4371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.4143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.3985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.3906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.3848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.2851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.2736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.2676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.2367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.2304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.2250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.2198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.2152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.2110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.2032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.1991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.1951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.1912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.1873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.1830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.1788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.1750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.1715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.1680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.1651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.1625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.1597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.1569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.1539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.1511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.1484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.1457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.1407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.1359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.1316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.1294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.1272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.1248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.1225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.1201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.1178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.1136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.1116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.1066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.1050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.1033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.0997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.9021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3740 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 530/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.7811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 36s 306ms/step - accuracy: 1.0000 - loss: 2.8920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.9195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.7338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.7084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.6893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.6931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.8558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3763 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 531/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 3.9017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.8899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.7785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.4854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.3806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.2904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.1325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.0248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.8556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.8414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.8266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.8132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.8015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.7893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.7781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.7682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.7592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.7494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.7400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.7311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.7227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.7141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.7053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.5958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.5675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.5630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.5580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.5509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.5460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.5414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.3132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3789 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 532/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.4678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.4454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.3099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.1647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.1092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.1017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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2.0537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.0487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.0379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.0350e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.0161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.0150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.0136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.0121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.0065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.0043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.0022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.0013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9966e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.9953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3811 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 533/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.0451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.0150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.0908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.5738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.7029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.7388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.7713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.8285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.8536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.8765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.8975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.9666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.9810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.9939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.0489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.0632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.0765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.0889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.2003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.2034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.2062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.2088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.2111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3824 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 534/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.6057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.5516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.0006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.0029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.0040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.0082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.0090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3850 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 535/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.0411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.8139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.7522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.7602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.7595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.7587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.7577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.7565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.7535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.7516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.7508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.7501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.7492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.7483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.7472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.7462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.7210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3873 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 536/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.5043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.5473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.4599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.4125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3890 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 537/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3906 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 538/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 1.3626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3923 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 539/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.9068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.0003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 4.3945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 4.7162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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5.9437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.1253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.1490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.1661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.1821e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.1790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.1675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.1413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.1266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.0945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.0769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9621e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.6454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.6245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.6039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.5832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.5627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.5223e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.3643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.3450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.3260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.3071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.0912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.0742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.0461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3892 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 540/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.7134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.4101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.4021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.2971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3929 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 541/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.2309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.1450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3973 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 542/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 1.0536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.8711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.9393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.9866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.0370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.0461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.0477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.0046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.9869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.9667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.9446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.9212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.7149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.6896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.6641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.6388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.6136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.5159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.3791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.9927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.9807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.9223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.9111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.9000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.7020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3958 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 543/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.1467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.0429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.0316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.0233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.0149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 1.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.0049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.0032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.9841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.9546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.9265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.8969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.8650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.8097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.7830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.7596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.7412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.7321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.7205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.7063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.6914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.6751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.6573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.6385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.6207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 9.6070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 9.5952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 9.5885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.5842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.5808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.5751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.5685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.5607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.5532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.5457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.5377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.5235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.5165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.5101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.5047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.5005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.4967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.4890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.4850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.4804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.4754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.4702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.4650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.4593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.4543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.4496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.4134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.4102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.4067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.4035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.4014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.3996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.3975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.3489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.3443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.3383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.3365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.3332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.3258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.1137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3983 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 544/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 1.0497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.9923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.6845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.5672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.4555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.3641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.3472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.3157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.2938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 9.2735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.2614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.2398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.2153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 9.1917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.1692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.1473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.1208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 9.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 9.0372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 9.0263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 9.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 8.9975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 8.9840e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.6999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.6996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.6992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.6991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.6991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.6996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.7000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.7002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.7001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 8.7775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.8527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.9255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 8.9965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.0654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.1324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 9.1978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.2616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.3239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.3844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 9.4435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.5014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.5581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 9.6133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.6671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.7203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.7725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 9.8237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.5920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3934 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 545/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 1.9114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.8743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.9985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.3573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.4818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.5183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.5151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.3933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.3752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.3569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.3406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.2755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.2589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.2422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.2260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.2122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.0958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.9936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4012 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 546/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.6472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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2.6150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4027 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 547/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.0723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.7397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.5226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.3255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.0062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.9213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.8419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.7117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.7170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.7152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.7089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.7037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.6967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.6910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.6922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.6940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.6946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.6927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.6907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.6895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.6876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.6853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.6844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.6861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.6872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.6875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.6863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.6860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.6850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.6833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.6813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.6803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.6787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.6770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.6758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.6768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.6772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.6783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.6790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.6800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.6845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.6833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.6827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.6817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.6808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.6799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.6797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.6787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.6774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.6759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.6742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.6673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.6537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.6530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.6523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.6517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.6512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.6506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.6499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.6493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.6487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.6481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.6478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.6477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.6475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.6472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.6468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.6462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.6455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.6436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.6426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.6417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.5322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4033 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 548/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 7.0744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.4048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.1440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.2949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.3055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.0514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.8101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.8028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.7842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.8774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.0328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.0973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.1570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.2101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.4338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.4688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.5004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.5281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.5521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.5735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.5933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.6193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.6444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.6683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.6913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.7969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.8095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.8209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.8311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.8405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.8497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.8591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.8669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.8734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.8792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.8852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.9194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.9112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.9033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.9002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.8598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.2781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4043 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 549/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 9.7192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.0403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 9.0477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 8.7365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 8.5424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 8.3584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 8.2501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 8.1505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.0294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.9312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.8453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.7646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.6986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.6521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.6160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.5790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.5396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.5021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.4660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.4263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.3855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.3504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.2975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.2753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.2578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.2472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.2358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.2028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.1908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.1773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.1648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.1540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.1320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.1291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.1257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.1220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.1174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.1119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.1062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.1015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.0978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.0937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.0607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.0548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.0518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.0487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.0459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.0282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.0201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.9243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.9236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.9227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.9217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.9207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.9188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.8055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4067 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 550/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.5216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.2725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.3465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.8628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.6583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.4706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.3420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.2073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.1132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.9793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.9257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.8039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.7717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.7661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.7092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.6973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.6862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.6757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.6630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.6497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.6351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.6210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.6065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.5925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.5575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.5488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.5412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.5340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.4997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.4934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.4866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.4803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.4735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.4669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.4609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.4555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.4512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.4470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.4423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.4378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.4327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.4275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.4223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.4178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.4138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.4106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.4078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.4056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.4037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.4013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.3985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.3957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.3931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.3902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.3778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.3767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.3753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.3737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.3719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.3700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.3681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.3658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.3634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.3611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.3589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.3569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.3550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.3534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.3519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.3501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.3406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.3390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.3373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.3341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.3327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.3310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.3293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.3274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.3254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.3234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.3213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.3192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.3152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.3132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.3114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.3096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.3076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.3057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.3037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.3024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.3010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 6.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4083 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 551/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.5399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.5572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 6.6109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.5246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.4469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.4045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.3570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 6.2891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.2310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.1835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.1379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.8959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.8894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.8833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.8778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.8721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.8672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.8493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.8483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.8469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.8450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.8441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.8435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.8429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.8423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.8292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.8286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.5558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4101 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 552/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.2505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.1018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.8496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.6324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.4216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.2396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.1829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.1217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.9919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.9379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.8125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.7359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.7111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.6876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.6619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.7744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.0194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.1391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.1881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.4202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.3628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.3590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.3549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.3515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.3478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.3440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.3300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.3194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.3154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.3112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.3069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.3023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.2442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.2397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.2305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.2260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.2217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.2001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.7225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4108 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 553/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.6063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.7713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.8180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.6692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.5269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.4055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.3064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.2947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.2500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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5.1304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.1234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.1194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.1151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.0975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.0934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.0884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.0321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.0227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.0204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.0179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.0122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.0085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.0049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.0014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.9886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.9790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.9759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.9728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.9694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.8929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.8688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.8535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.8526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.8490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.7360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4127 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 554/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.7091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.9869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.0995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.5105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.6492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.7357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.7550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 5.7617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.7337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.6979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 5.6655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.6217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 5.5835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.5507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.5330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.5075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.4805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.4644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.4494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.4308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.4095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.3702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.3399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.3283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.3122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.3017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.2921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.2818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.2705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.2458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.2222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.2110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.2006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.1913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.1814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.1712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.1607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.1503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.1394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.1283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.1171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.1067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.0367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.0290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.0210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.0134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.0062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.9822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.9769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.9715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.9662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.9607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.9552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.9496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.8963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.8925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.8891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.8856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.8824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.8793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.8762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.8733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.8702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.8543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.8479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.8447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.8416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.8387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.8247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.8224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.8202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.8180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.8158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.8136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.8027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.8004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.7981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.5138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4144 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 555/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.5410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.3366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.2851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.2654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.2369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.2028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.1448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.1177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.1035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.0986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.1028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.1019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.0964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.0980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.0885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.0882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.0885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.0888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.9232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4161 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 556/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.8395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.7465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.7062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.6695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.6445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.6484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.6535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.6684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.6884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.8077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.8383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.9081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.9178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.9262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.9344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.9433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.9483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.9536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.9573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.9612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.9641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.9658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.9668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.9680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.9685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.9689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.9694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.9703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.9704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.9699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.9689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.9719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.9741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.9756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.9767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.9777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.9781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.9784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.9790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.9797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.9796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.9790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.9787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.9783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.9776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.9765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.9753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.9745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.9734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.9728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.9723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.9719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.9714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.9707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.9698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.9688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.9674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.9660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.9645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.9632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.9618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.9607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.9598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.9591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.9582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.9571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.9561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.9550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.9538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.9524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.9509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.9495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.9480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.9466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.9441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.9426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.9411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.9394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.9379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.9362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.9346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.9329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.9312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.9296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.9280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.9267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.9256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.9243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.9230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.9215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.9201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.9171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.9156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.9141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.9124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.9109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.9038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.9025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.9012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.8999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.8986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.8960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.7458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4179 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 557/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 3.3444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.7476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.8700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.8312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.7731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.6714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.6342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.5970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.5667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.5468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.5237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.4849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.4173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.4186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.4194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.4979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.4981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.4982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.4983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.4937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4197 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 558/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.4439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.5124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.4007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.3289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.3262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4211 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 559/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.8367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.7893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.6896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.2472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.2426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.2370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.2286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.2141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.2081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.1744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.1592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4228 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 560/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.8946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.9133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.4037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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3.2859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.1457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.1331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.1291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.1080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.1052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.1024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.0994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.8990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4241 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 561/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.5902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.5592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.2232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.9433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.9398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.9388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.9378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.9367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.9324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.9312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.9292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.9283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.9272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.9261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.7923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4255 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 562/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 3.2961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.4938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.4972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.9026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.7992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.7983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.7974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.7966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.7957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.7948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.7939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.7930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.7922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.7914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.7908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.7902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.7895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.7889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.7882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.7875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.7868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.7860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.7851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.7843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.7836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.7828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.6910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4270 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 563/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.8345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.7769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.7676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.7583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.7487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.7379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.7298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.7217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.6957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.6789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.6274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.6271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.6269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.5998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.4717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4288 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 564/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.5212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.5659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.5846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.5286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.4639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.4098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.3702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.3009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.2766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.2855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.2966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4302 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 565/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.0223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.1554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.2138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.0607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.0693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.0703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.7336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.7955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4297 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 566/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.3583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.0757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 3.3696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.9023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.5693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.3184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.1216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.1829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.1483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.1162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.0864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.0588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.0329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.0089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.8644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.6544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.4562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.2692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 9.0922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.9254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.7665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.6150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.4707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.3339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.2030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.0782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.8450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.7360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.6313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.5308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.3412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.2514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.1654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.0827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.9257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.8516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.7801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.7108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.6436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.5784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.5152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.4538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.3941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.3362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.2801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.2255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.1727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.1215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.0717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.0232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.9758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.9300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.8854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.8419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.7996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.7582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.7179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.6785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.6401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.6027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.4956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.4613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.4278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.3950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.3628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.3004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.2701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.2405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.2116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.1835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.1559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.1287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.1020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.8827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.8172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.7961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.7754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.7550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.7350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.7154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.6962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.6772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.6586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.6404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.6224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.6047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.5872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.5699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.5529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.5685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4315 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 567/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.6420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.0924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4334 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 568/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.1799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.2160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.9948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.9938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.9974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.9988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.0017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.0054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.0109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.0122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.0149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.0567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4348 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 569/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.4656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.7615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4365 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 570/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.8805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.9778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.0117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.9987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.9924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.9847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.9662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.9217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.9157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.9109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.9024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.8742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.8699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.8658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.8596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.8581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.8570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.8558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.8544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.8491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.8475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.8421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.8413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.8407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.8306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.7500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4378 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 571/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 307ms/step - accuracy: 1.0000 - loss: 1.9623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.0507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.0068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.9683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4393 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 572/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.2644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.6476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.2022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.1576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.1191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.0376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.0155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.9949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.9772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.9602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.9435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.9267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.9113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.7052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.7020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.6084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4407 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 573/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.4873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.5127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4416 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 574/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 1.4373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.5430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.6263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.6736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.7111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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2.5365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.3188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.2432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.2367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.2301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.2235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.2173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.2123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.2072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.2019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.1153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.1077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.1039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.1000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.6434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4429 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 575/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.5077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4440 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 576/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.5035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.7470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.6195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.5815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.5749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.5538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.5506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.5456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.5156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.5108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.5059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.5013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.4901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.4860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.4822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.4783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.3402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4454 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 577/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.5263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.5001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.5013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4469 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 578/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.3593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.1655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.1320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.1304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.1280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.1264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.1239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.1234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4481 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 579/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.4166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.8964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.6355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.4315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.0475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4499 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 580/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 9.2695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.5576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.8056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.8994e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.8882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.9227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.9380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.9157e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.8448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.8491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.8692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.8746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.8768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.8828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.9058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.9145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.9260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.9314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.9374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.9425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.9558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.9576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.9648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.9732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.9826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.9960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.0233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.0252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4504 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 581/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.4600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4517 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 582/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.1050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.0542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.0231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.0191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.0250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.0262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.0335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.0375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.0389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 9.6426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4533 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 583/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 306ms/step - accuracy: 1.0000 - loss: 1.2788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.8417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.8348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.8190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.7790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.7562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.6732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.6459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.6236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.6091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.6049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.5935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.5830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.5338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.4988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.4868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.4796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.4812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.4719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.4502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.4446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.4396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.4398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.4404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.4404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.4397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.4384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.4366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.4341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.4332e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.4319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.4308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.4292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.4300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.4308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.4319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.4321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.4317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.4394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.4464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.4528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.4601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.4929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.5008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.5086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.5161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.5249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.5329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.5404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.5476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.5538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.5600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.5658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.5708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.5757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.5802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.5965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.6000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.6030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.6058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.6089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.6119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.6151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.6182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.6210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.6242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.6268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.6291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.6314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.6335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.6352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.6368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.6382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.6395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.6520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.6536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.6553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.6567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.6581e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.6596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.6608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.8033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4543 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 584/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.0860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.7867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.5180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.3331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.1615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.0019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.8836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.8192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.7507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.7297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.6313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.6238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.6171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.6156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.6190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.6197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.6189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.6168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.6136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.6092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.6038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.5991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.5959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.5948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.6012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.6078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.6263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.6908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.7025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.7133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.7231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.7322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.7399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.7480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.7711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.7884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.7932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.7970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8581e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.8641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.8639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.8638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.8646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.8653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.8656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8655e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.8633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.8623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.8614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.8603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.8593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.8584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.8579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.8525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.8509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.8493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.8478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.8465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.8454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.8446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.8438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.8417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.8378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8443e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.0307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4553 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 585/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.2650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 7.6024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.2490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.6268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.8238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.8767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.3879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.2262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.1478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.0961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.9978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.0297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.1565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.2149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.0940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.2653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.2962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.4390e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1086e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.8754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.8864e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1185e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.6622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.2797e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1210e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3567e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6297e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9482e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.2935e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.6670e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0766e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.5027e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.9518e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4206e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.8980e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.3817e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8768e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.3866e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.9077e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.4333e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.9692e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.5100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.0547e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.5951e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0132e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0666e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1193e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1712e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2222e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.2726e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3223e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3711e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4190e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4661e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5124e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5574e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6013e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6443e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 1.6864e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 1.7276e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 1.7678e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 1.8070e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 1.8455e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 1.8831e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 1.9196e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 1.9553e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 1.9901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 2.0240e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 2.0569e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 2.0890e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 2.1205e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 2.1511e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 2.1810e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 2.2101e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 2.2385e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 2.2662e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9998 - loss: 5.5677e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9769 - val_loss: 0.3351 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 586/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9999 - loss: 3.2458e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9999 - loss: 2.9532e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 0.9999 - loss: 2.7747e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9999 - loss: 2.5866e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9999 - loss: 2.4355e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 0.9999 - loss: 2.3695e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 0.9999 - loss: 2.3052e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9999 - loss: 2.2472e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9999 - loss: 2.2020e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9999 - loss: 2.1672e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9999 - loss: 2.1464e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9999 - loss: 2.1233e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 0.9999 - loss: 2.1013e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9999 - loss: 2.0793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9999 - loss: 2.0588e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9999 - loss: 2.0394e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9999 - loss: 2.0206e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9999 - loss: 2.0084e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9999 - loss: 2.0020e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 0.9999 - loss: 1.9965e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9999 - loss: 1.9889e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9999 - loss: 1.9795e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9999 - loss: 1.9706e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9999 - loss: 1.9629e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9999 - loss: 1.9596e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9999 - loss: 1.9574e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9999 - loss: 1.9542e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9999 - loss: 1.9492e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9999 - loss: 1.9426e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9999 - loss: 1.9349e-04 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9999 - loss: 1.9259e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 0.9999 - loss: 1.9301e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9999 - loss: 1.9338e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9999 - loss: 1.9380e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 0.9999 - loss: 1.9419e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9999 - loss: 1.9456e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9999 - loss: 1.9493e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 0.9999 - loss: 1.9531e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9999 - loss: 1.9565e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9999 - loss: 1.9600e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9999 - loss: 1.9636e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 0.9999 - loss: 1.9673e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9999 - loss: 1.9709e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9999 - loss: 1.9743e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 0.9999 - loss: 1.9775e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 1.9805e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 1.9836e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 1.9866e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 1.9896e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 1.9925e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 1.9950e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 1.9971e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 1.9991e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 2.0010e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 2.0029e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 2.0047e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 2.0062e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 2.0075e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.0086e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.0095e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.0103e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.0109e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 2.0113e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 2.0114e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 2.0114e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 2.0112e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 2.0108e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 2.0102e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.0094e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.0084e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.0073e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.0060e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 2.0046e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 2.0031e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 2.0015e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 1.9997e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 1.9978e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 1.9957e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 1.9936e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 1.9913e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 1.9888e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 1.9863e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 1.9837e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 1.9809e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 1.9781e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 1.9752e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 1.9722e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 1.9691e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 1.9659e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 1.9627e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 1.9594e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 1.9560e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 1.9526e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 1.9492e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 1.9456e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 1.9421e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 1.9384e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 1.9348e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 1.9311e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.4904e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3354 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 587/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.7652e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.8767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.9678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8091e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6401e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4499e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4126e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3258e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.2145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1879e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1627e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0152e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9956e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9764e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8234e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8088e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7803e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7663e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6887e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6769e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6545e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6434e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6216e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6107e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.5899e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5713e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.5533e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5356e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.5268e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5182e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5096e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.5011e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4928e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4529e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4303e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4085e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3944e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.3739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.3541e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3223e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2983e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.2925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2810e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2696e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2370e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2317e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2214e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2162e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2111e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2060e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2010e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1959e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5992e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3500 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 588/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0856e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0942e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 303ms/step - accuracy: 1.0000 - loss: 1.0408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.0254e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.0180e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.9400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 9.9036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 9.8658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 9.8356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 9.8110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 9.8060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 9.7849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 9.7578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.7270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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9.5233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.5076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.4889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.4693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.4487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.4269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.4053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.3826e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.8318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.8224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.8131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.8041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.7951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.7860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.7681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.7593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.7507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.7419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.7331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.7245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.7160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.7077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.6505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.6422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.6340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.6259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.6179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.6099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.6021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.5651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3593 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 589/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.4576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.0485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.1591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.0159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.8164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.7129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.5297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.2282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.2087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.1526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.1187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.1038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.9658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.9602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.9549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.9501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.9451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.9172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.9120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.9067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.9013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.8962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.8914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.8868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.8823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.8784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.8745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.8702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.8656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.8608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.8559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.8508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.8456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.8406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.8359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.8311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.8264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.8220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.8090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.8043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.7996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.7507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.7480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.7451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.7422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.7395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.7167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.7155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.7144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.7133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.7120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.7108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.7097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.7086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.5791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3628 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 590/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.5520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.8042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.9493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.9115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.8952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.6905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.6069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.6033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.6068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.6046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.5998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.5918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.5825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.5737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.5641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.5567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.5526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.5480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.5497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.5523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.5520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.5504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.5488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.5475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.5469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.5476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.5476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.5475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.5467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.5453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.5437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.5415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.5400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.5391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.5379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.5368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.5358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.5351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.5338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.5322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.5301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.5280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.5258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.5232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.5207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.5186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.5109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.5092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.5073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.5052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.5029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.5005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.4980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.4845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.3980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.1572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3685 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 591/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.0741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.1159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.9621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.8934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.7009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.6923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.6850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.6645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.6439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.6408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.6385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.6370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.6349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.6322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.6294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.6167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.6140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.6114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.6090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.6081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.6075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.6064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.6011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.6002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.5848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.5836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.3733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3734 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 592/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.9252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.2069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.4039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.2034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.1900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.1797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.1701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.1632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.1587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.1508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.1414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.0980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.0881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.0573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.0521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.0469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.0408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.0368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.0339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.0171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.0151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.0130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.0111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.0089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.0066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.0039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.0014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.9975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.9967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.9966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.9959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.9953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.9943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.9933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.9923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.9911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.9901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.9894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.9885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.9877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.9870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.9864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.9856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.9848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.9842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.9835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.9827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.9818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.9808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.9800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.9790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.9781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.9772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.9764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.9754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.9745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.9733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.9722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.9695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.9681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.9668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.9654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.9640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.9627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.9616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.9592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.7934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3764 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 593/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.5409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.4359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.0029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.8158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.8030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.7642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.6389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.9541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.1506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.2096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.4649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.5091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.5516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.5920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.6670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.8812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.9068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.9314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.9548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.9771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.9983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.0901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.1059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.1494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.1982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.8951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3786 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 594/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.4798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.3308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.8058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.7729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.7583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.7363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.7309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.7261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.7210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.7160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.7116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.7078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.7036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.6734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.6694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.6282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.6253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.6226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.5387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3816 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 595/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.2341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.3606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.2735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.1890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.1700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.1589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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2.1454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.0649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3841 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 596/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.0070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.0440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.3846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3753 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 597/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.5494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.5524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.6863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.6573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.6444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.4951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.4571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.4577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.4591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.4600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.4848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.4869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.4878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.4885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.4888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.4887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.4883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.4877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.4868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.4857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.4843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.4830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.4816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.4801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.4787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.4772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.4756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.4738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.4718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.4698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.4678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.4656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.4613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.4591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3864 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 598/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.7963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.8349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.8101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.7900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.8124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.8243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 1.8239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.8146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.8071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.8042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.7996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.7951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 1.7923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.7928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.7903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.7868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 1.7819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.7777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.7730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.7673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.7500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.7477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.7450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.7432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.7451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.7404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.7400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.7398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.7394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.7385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.7380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.7375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.7368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.7362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.7335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.7331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.7326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.7215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.6389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3893 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 599/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.5023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.5062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.5099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.5135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.5173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.5210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.5245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.5279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.5311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.5342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.5373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.5402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.5430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.5549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.5570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.5595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.7210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3916 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 600/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 1.3865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.4365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.3054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.2944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.2911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.2896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.2878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.3938 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 601/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3876 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 602/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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1.3916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3774e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3471e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3210e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.3083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.3053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.3031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.2120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3953 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 603/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.3411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.3544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.3237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.3039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.2770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.2519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.2290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.2069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.1652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.1382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.1360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.1339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.1294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.1146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3988 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 604/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.0919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.1181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.1290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.1278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.9954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.9789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.9645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.9517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.9400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.9264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.8974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.8826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.8672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.8517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.8373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.8237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.8102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.7978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.7860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.7776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.7680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.7589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.7495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.7395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.7292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.7182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.7074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.6976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.6888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.6802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.6720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.6322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.6246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.6166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.5936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.5860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.5791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.5742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.5688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.5634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.5577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.5524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.5471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.5417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.5371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.5331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.5287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.5244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.5203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.5167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.5128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.5089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.5046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.5008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.4968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.4926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.4883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.4841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.4799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.4758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.4722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.4687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.4649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.4622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.4594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.4564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.4532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.4497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.4462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.4428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.4394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.4358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.4233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.4132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.4218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.8836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4001 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 605/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.4600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.8270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.1572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.2213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 8.2289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 8.1845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 8.1179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 8.0450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.9696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.9482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.9651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.9752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.0014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.0721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.1076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.1590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.1679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.1718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.1788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.1882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.1945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.2009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.2103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.2219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.2307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.2372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.2410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.2465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.2513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.2547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.2601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.2663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.2724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.2982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.3079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.3236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.3295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.3336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.3358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.3385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.3419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.3448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.3480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.3513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.3549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.3574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.3594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.3606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.3616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.3624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.3835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.3839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.3846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.3852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.3866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.3881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.3890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.3897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.3905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.3912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.3928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.3945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.3962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.3975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.3991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.6450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4014 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 606/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.9823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.7504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.9540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.7228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.4675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.2495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.0610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.8914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.7578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.6620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.8953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.0688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.2125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.3342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.5493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.5957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.6355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.6629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.6877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.7071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.7171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.7250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.7300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.7343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.7331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.7308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.6866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.6704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.6540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.6368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.6194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.5916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.5758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.5595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.5424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.5255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.5079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.4893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.4710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.4537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.4359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.4188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.3871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.3709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.3544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.3378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.3044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.2869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.2700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.2540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.1945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.0971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.0845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.0720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.0603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.0384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.0054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.9027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.8934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.7921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.7848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.7775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.7701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.7629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.7560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.7492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.7424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.7357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.7106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.7044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.6981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.6918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.6853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.6789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.6726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.6663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.9149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4034 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 607/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.6349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.2441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.1101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.1331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.0813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.1014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.0431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.9477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.8110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.6990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.6581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.6199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.5773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.5362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.5033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.4682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.4016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.3668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.3086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.2822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.2579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.2370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.2147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.1920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.1296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.1140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.1014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.0904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.0788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.0676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.0575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.0017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.9776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.9706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.9639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.9580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.9529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.9469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.9411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.9347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.9281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.9210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.9130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.9050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.8897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.8826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.8758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.8696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.8630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.8564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.8495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.8425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.8363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.8236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.8175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.8115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.8055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.7942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.7882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.7724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.7671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.3632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.6463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.1819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.4357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.6810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.9178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.4624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4042 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 608/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 9.5212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 9.7294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 9.8350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.7850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.6771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.5547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.4605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 9.3439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.2506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.1900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.1293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.0890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.0539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.0287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.9791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.9743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.9613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.9427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.9283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.9074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.8981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.8949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.8914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.8839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.8747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.8636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.8520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.8443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.8329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.8208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.8108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.7994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.7908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.7852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.7811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.7758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.7700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.7629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.7554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.7468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.7368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.7275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.7189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.7100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.7020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.6944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.6879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.6804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.6441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.6367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.6297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.6225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.6152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.6087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.6024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.5959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.5893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.5823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.5753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.5684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.5609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.5536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.5468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.5403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.5150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.5083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.5015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.4951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.4887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.4822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.4759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.4698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.4637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.4578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.4522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.4467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.4410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.4115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.4058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.4002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.3946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.3891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.3838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.3797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.3625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.3583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.3539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.3499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.3461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.3423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.3386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.3316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.3281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.3245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.3208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.8373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4056 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 609/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.2619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.0882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.0682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.8435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.7524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.6767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.6392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.5898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.5181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.4713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.4555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 7.4416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.4322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.4294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.4403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.4408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.4432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.4499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - 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7.4195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.4166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.4109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.4041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.3978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.3821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.3717e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.2588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.2563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.2537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.2514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.2491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.2464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.2439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.2337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.2310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.2286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.2240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.2220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.2201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.2180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.2158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.3271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.6419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.8405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.8070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.7491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.2202e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9770 - val_loss: 0.4358 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 610/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.0144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.0098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.1413e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.7058e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.4548e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8756e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.0919e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.3021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.9526e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.7563e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.3650e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.8105e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.1286e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.4893e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.8187e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.3287e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 7.7816e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 8.2062e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 8.6441e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.0371e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.4713e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.0051e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.0653e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.1248e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.1834e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.2450e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3075e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3734e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4417e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.5105e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.5827e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6554e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7265e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.8041e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.8819e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.9617e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.0406e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 0.9999 - loss: 2.1189e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9999 - loss: 2.1964e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 0.9999 - loss: 2.2707e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 0.9999 - loss: 2.3436e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 0.9999 - loss: 2.4148e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9999 - loss: 2.4848e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9999 - loss: 2.5533e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 0.9999 - loss: 2.6184e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9999 - loss: 2.6835e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9999 - loss: 2.7470e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9999 - loss: 2.8082e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 0.9999 - loss: 2.8673e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9999 - loss: 2.9244e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9999 - loss: 2.9790e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 0.9999 - loss: 3.0313e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9999 - loss: 3.0816e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9999 - loss: 3.1296e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 0.9999 - loss: 3.1753e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9999 - loss: 3.2182e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9999 - loss: 3.2587e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9999 - loss: 3.2975e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 0.9999 - loss: 3.3347e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9999 - loss: 3.3697e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9999 - loss: 3.4028e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 0.9999 - loss: 3.4340e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9999 - loss: 3.4636e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9999 - loss: 3.4915e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 0.9999 - loss: 3.5178e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9999 - loss: 3.5450e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9999 - loss: 3.5709e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9999 - loss: 3.5956e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 0.9999 - loss: 3.6189e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9999 - loss: 3.6418e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9999 - loss: 3.6635e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 0.9999 - loss: 3.6843e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9999 - loss: 3.7046e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9999 - loss: 3.7241e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9999 - loss: 3.7427e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 0.9999 - loss: 3.7604e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9999 - loss: 3.7775e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9999 - loss: 3.7938e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 0.9999 - loss: 3.8096e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9999 - loss: 3.8245e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9999 - loss: 3.8387e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 0.9999 - loss: 3.8528e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9999 - loss: 3.8663e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9999 - loss: 3.8792e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9999 - loss: 3.8919e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 0.9999 - loss: 3.9040e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9999 - loss: 3.9155e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9999 - loss: 3.9263e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.9999 - loss: 3.9367e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.9469e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.9566e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.9658e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 0.9999 - loss: 3.9743e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9999 - loss: 3.9824e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9999 - loss: 3.9900e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 0.9999 - loss: 3.9973e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9999 - loss: 4.0046e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9999 - loss: 4.0115e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 0.9999 - loss: 4.0179e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 4.0239e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 4.0295e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 4.0346e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 0.9999 - loss: 4.0393e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 4.0437e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 4.0477e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 0.9999 - loss: 4.0514e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 4.0550e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 4.0585e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 0.9999 - loss: 4.0617e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 4.0647e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 4.0672e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 4.0694e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 0.9999 - loss: 4.0713e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9999 - loss: 4.0730e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9999 - loss: 4.0745e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 0.9999 - loss: 4.0757e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 4.0767e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 4.0774e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 4.0778e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9999 - loss: 4.0780e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 0.9999 - loss: 4.1004e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9770 - val_loss: 0.3464 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 611/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1297e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0061e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 9.3042e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 8.9325e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 8.7282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.8239e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.9144e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.0412e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.0457e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.0122e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.0189e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.9864e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.9588e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.9007e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.8286e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.7395e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6409e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.5391e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.4406e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.4011e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.3500e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.2968e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.2579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.2282e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.2123e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.1890e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.1587e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.1224e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.0825e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.0387e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9534e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.9121e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8692e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8252e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.7797e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.7376e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.6941e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.6494e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.6034e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.5568e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.5112e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.4650e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.4183e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.3712e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.3254e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.2794e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.2335e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.1879e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.1423e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0969e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0515e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0063e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.9613e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.9166e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.8721e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.8279e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.7840e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.7405e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.7000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.6597e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.6198e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.5801e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.5407e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.5016e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.4628e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.4244e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3863e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3485e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.3111e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2741e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2374e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2011e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1652e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.1296e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.0944e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.0596e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.0251e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.9910e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.9573e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.9239e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.8908e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.8581e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.8258e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.7938e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.7622e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.7309e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.7000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.6694e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.6391e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.6092e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.5796e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.5503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.5213e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4927e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4644e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4365e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4088e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.3814e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.3544e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.3276e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.3011e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.2748e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.2489e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.2232e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1978e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1727e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1478e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1232e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0988e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0747e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0509e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0272e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.0039e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9807e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.9578e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9352e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9127e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8905e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8685e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.2507e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3614 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 612/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5853e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6770e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5934e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4654e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4444e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.4058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.3921e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.3843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3750e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.3679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3573e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3520e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3203e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3089e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3049e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3007e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2920e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2874e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2829e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2789e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2748e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2710e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2641e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2605e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2570e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2535e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2429e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2394e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2292e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2198e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2167e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2137e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2106e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1957e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1902e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1876e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1795e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1771e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1746e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1697e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1673e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1650e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1628e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1606e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1684e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1777e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2110e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2323e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2388e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2451e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2678e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2728e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2909e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2989e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3027e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.3064e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3166e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.3197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3227e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3283e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3309e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3334e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3404e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3446e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3483e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3501e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3517e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3532e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3562e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.5285e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3639 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 613/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.3399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.6334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.6860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.5147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.3585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.2157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.1107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.9839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.8619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.7597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.7058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.6519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.6057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.5763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.5554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.5274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.5039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.2505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.2366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.2044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.1885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.1749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.1621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.1394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.1299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.1200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.1102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.0692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.0487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.0389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.0291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.0205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.0146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.0090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.9974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.9857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.9675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.9262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.9007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.8948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.8886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.8828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.8774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.8722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.8668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.8613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.8556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.8499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.8438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.8376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.8312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.8252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.8191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.8132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.8075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.8019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.7963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.7905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.7849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.7792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.7733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.7674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.7615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.7558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.7500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.7442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.7385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.7330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.7096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.7036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.6975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.6913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.6734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.6498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.6439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.6379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.6317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.6255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.6193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.6132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.6071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.8771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3717 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 614/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.8160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.8768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.9888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.8837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.7494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.5339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.4489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.0740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.0467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.0201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.9699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.9452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.9216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.9029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.8869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.8257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.8131e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.4528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.4460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.4427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.4397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.4367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.4209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.4176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.4142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.4013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.3982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.3951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.3919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.3887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.3855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.3822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.9867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3777 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 615/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.1463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.1433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.9555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.9392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.9269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.9135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.9039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.8540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.8291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.7774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.7383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.7350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.7313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.7274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.7229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.7181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.7132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.7087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.7042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.6998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.6958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.6918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.6830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.6784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.6739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.6693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.6646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.6601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.6558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.6515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.6473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.6395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.6355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.6316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.6276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.6236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.6196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.6156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.5972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.5939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.5904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.5870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.5835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.5800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.5659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.5556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.5458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.5323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.5221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.1462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3819 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 616/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.0027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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2.9584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.9235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.9187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.9146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.9106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.9071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.9038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.8770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.8657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.7993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.7976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.7959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.7943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.7848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.7835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.7822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.7807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.7794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3862 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 617/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.5778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.7816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.7898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.7904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.7706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.7199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.6045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.4582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.3662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.3711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.3758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.3804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.3848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.3890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.3929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.3967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.4036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.4068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.4169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.4206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.4244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.4280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.4315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.4347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.4379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.4408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.4436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.4463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.4490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.4516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.4542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.4567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.4592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.4616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.4640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.4663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.4725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.4743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.4762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.4781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.6937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3842 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 618/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.5463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.5864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.3977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.2955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.2840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.2794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.2761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.2713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.2266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.2173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.2014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.9878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3900 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 619/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.2608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.0142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.8491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.8335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.8194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.8139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.8116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.8192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.8234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.8092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.5692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.2107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.7560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.2207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.6197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.9620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.2572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.5123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.9216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.0851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.2260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.3470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.4499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.5374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.6117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.6751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.7284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.7728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.8098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.8399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.8632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.8809e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.5732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.5475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.5216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.4953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.4418e-07 - 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0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.6814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.6572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.6331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.6092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.5617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.5381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.5146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.4913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.4683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.4454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.4227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.3780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.3339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.3122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.1849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.7042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3930 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 620/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.1640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.1264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.0933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.0618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.0326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3953 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 621/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.7481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.8056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.7772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.6805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.6536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.6267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.6103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.6014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.5817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.5789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.5478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.5452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.5432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.5422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.5419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.5410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.5397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.5383e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.5353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.5345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.5338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.5332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.5328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.5325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.5323e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3980 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 622/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.4659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.4331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.3891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4004 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 623/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.0075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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1.3541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2160e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.2196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.2268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4019 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 624/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 1.4387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 1.3923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.3260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.2755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.1909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.1725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.1595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.1510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.1417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.1347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.2733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.3074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.3357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.3593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.3801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.3971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.4914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.4934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.4850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.4285e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4049 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 625/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.2410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.9857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.9575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.9463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.9355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.9258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 9.9152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 9.9040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.8927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.8837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.8751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.8677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.8597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.8530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.8475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.8429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.8374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.8322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.8273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.8225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.8191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.8182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.8170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.8174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.8170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.8163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.8155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.8128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.8110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.8089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.8062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.8029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.7990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.7842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.7748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.7339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.7303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.7267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.7233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.7200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.7163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.7125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.7088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.7054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.7021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.6673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.6640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.6606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.6577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.6549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.6518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.6487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.6465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.6443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.6419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.6395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.6370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.6352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.6332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4071 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 626/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.8618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.7100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.6070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.2653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.2326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.9967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.9778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.9204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.9054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.8903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.8757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.8623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.8565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.8498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.8431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.8382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.8342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.8310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.8268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.8221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.8174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.8112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.8039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.7974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.7935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.7888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.7843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.7800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.7766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.7724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.7678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.7629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.7574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.7511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.7440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.7374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.7314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.7251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.7190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.7144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.7100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.7050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.7000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.6958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.6912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.6866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.6814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.6764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.6720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.6672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.6630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.6593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.6557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.6516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.6474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.6429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.6383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.6286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.6241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.6199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.6161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.6126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.6093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.6064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.6032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.5997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.5963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.5928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.5889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.5848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.5808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.5769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.5729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.5691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.5655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.5620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.5585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.5548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.5509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.5470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.5430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.5388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.5346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.5306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.5267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.5229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.5193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.5159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.5123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.5088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.5054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.5019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.4982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.4945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.4907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.4872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.4836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.0541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4091 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 627/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.9612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.4738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.6753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.5304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.4361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.1627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.0615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.0204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.9932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.9862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.9828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.9793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.9687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.9580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.9322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.9402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.9565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.9670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.9754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.9836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.9919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.9954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.9966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.9961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.9922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.9883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.9849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.9803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.9791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.9824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.9853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.9868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.9875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.9875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.9762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.9733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.9707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.9685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.9665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.9633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.9461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.9299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.9140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.9106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.9092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.9079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.9059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.9035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.9015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.8999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.8984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.8971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.8970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.8969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.8963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.8954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.8942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.8927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.8851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.8835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.8822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.8814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.8808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.8798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.8786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.8719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.8660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.8640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.8622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.8581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.8568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.8553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.8538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.8521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.8506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.8491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.8477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.8465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.8452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.7014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4114 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 628/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.7029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.3587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.2320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.1264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.9309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.8499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.7734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.5518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.4896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.3912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.3506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.3142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.2761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.2402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.2059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.1117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.0814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.0535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.0258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.9998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.9760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.9542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.9318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.9094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.8869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.8654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.8436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.8222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.8019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.7829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.7640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.7479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.7327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.7189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.7046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.6906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.6766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.6633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.6497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.6368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.6243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.6126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.6011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.5803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.5706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.5605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.5509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.5415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.5321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.5225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.5128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.5032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.4859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.4779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.4701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.4625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.4551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.4478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.4404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.4329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.4251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.4172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.4097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.4025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.3954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.3885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.3819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.3756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.3691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.3626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.3561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.3495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.3368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.3306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.3246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.3186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.3083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.3033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.2983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.2933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.2884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.2835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.2785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.2734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.2685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.2639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.2592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.2546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.2501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.2457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.2412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.2366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.2320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.2275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.2230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.2183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.2138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.2094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.2050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 6.6751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4132 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 629/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.1445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.5342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.2382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.8212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 8.5080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.3394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.3191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.7929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.5936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.5852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.5773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.5617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.5541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.5468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.5395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.5324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.5254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.5186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.5119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.5053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.7376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4143 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 630/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.2027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.6082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.5610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.3766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.1967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.9577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.8674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.7252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.7169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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6.6273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.6233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.6155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.6083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.5645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.5318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.5072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.4314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.4147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.4088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.4030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.3965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.3841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.3790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.3737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.3686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.3643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.3603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.3559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.3513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.3467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.3422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.3376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.3332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.3139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.3106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.3075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.3043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.3012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.2981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.2946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.2911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.2876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.2844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.2812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.2782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.2754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.2493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.2474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.2417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.2319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.2298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.2277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.2259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.2240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.2221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.2206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.2191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.2175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.2158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.2141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.2124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.2108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.2091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.2073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.2055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.2036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.9807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4160 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 631/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.6976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.5959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.5299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.3720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.2624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.0523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.2951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.4522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.5673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.6687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 6.8439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.8897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.8989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.9057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.9038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.8983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.8918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.8869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.8799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.8718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.8645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.8586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.8491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.8385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.8267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.8136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.7879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.7755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.7669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.7568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.6964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.6877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.6786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.6704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.6618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.6536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.6270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.6017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.5092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.5053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.5012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.4968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.4924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.4878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.4676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.4626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.4578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.4533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.4396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.4206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.4158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.4111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.4064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.4018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.3974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.3933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.3764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.3722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.3679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.3637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.3517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.3377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.3276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.3137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.9031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4178 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 632/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.7441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.4263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.0817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 7.6193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.2783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.9910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.7760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.6032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.4565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.3393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.2542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.1736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.1058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.0198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.9819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.9425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.9069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.7765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.7259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.7228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.7183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.7128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.7072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.7007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.6927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.6847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.6803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.6741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.6681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.6637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.6609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.6568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.6523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.6474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.6428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.6372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.6314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.6258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.6207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.6149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.6095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.6051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.6010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.5963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.5916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.5865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.5815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.5762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.5705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.5648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.5596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.5364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.5318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.5276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.5232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.5187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.5141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.5096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.5053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.5008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.4888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.4848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.4304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.4278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.4251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.4224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.4199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.4177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.3996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.3981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.3967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.3951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.3934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.3825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.2357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4196 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 633/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.0070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.4221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.5069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.4392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.3408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.3192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.2851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.2219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.1567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.1049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.0557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.0619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.0690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.0678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.0625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.0547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.0475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.0423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.0362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.0311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.0272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.0143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.0102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.0055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.0011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.0045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.0076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.0213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.0332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.0444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.0555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.0655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.0746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.0839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.0934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.1018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.1094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.1162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.1222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.1274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.1321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.1370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.1418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.1541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.1667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.1713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.1739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.1750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.1749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.1747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.1747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.1726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.1720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.1718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.1721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.1724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.1725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.1724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.1606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.1600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.1596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.1591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.2556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4213 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 634/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.0406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.3663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.4421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.3893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.2900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.2478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.1182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.1438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.1466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.1273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.1186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.1165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.1145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.0951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.0819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.1030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.1238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.1433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.2857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.3183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.3787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.5016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.5213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.5396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.5570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.7783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.7788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.7790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.7794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.7797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.7799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.7788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.7758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.4984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4229 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 635/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.9111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.2325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.2879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.0141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.9140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.6363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.6291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.6284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.6258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.6193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.6102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.6014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.5802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.5766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.5723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.5684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.5635e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.4642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.4647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.4650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.4653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.4656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.4660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.4666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.4665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.4663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.4661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.4658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.4658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.4658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.4657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.4656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.4654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.4651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.4647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.4137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4244 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 636/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.3893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.3195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.3293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.2416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.1592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.0951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.0302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.1722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.3115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.4208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.5000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.5682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.6281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.6810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.7846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.7946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.8097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.8169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.8273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.8378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.8481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.8540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.8579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.8087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.8032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.6868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.2708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4259 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 637/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.5777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.9064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.2400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.2491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.2049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.1633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.1175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.0680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.0288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.9941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.9748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.9609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.9497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.9445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.9427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.9440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.9412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.9364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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3.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.8950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.8929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.8485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.8467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.8445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.8422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.8402e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8485e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.8027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4275 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 638/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 3.2145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.0140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.6350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.6494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.5988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.5336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.2694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.2263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.1283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.1038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.9861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.9651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.9477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.9298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.9148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.9015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.8781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.8659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.8534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.8413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.8289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.8176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.8069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.7996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.7701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.7647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.7591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.7540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.7485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.7374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.6842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.6814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.6789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.6611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.6567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.6480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.6412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.6389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.6238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.6219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.6065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.6018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.5990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.5977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.5964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.5949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.5935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.5921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.5907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.5882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.5869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.5858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.5846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.5834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.5822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.5810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.5797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.4244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4291 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 639/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.5053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.3008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.1607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.9411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.8685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.6936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.6629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.6306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.6140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.5897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.5825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.5754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.4889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.4820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.4755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.4685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.4609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.4536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.4469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.4400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.3994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.3724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.3715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.3703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.3430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.3436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.3445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.3447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.3442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.3285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4308 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 640/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.5432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.6526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.6036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.5602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.5085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.4529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.4048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.3837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.3688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.3507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.3376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.3265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.3216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.3117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.3027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.2915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.2798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.2669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.2554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 3.2205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.2164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.2131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.8791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 4.4769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.0149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.4995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 5.9377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 6.3342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.6941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.0232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.3229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.6003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.8543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.0872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.2999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.4950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 8.6738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.8374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 8.9873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 9.1295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.5167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 9.6406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.7569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.8645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 9.9638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.0056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.0142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.0221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.0293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.0363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.0429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.0491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.0548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.0649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.0694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.0736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.0986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.0975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.0930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 9.5512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4217 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 641/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.8010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.9264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.9086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.3652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.3620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.3613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.3302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.3280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.3266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.3247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.3227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.3200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.3151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.3131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.3112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.3097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.3084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.3064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.3045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.3032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.3016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.2994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.2970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.2951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.2938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.2891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.2881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.2869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.2860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.2847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.2832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.2777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.2740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.2707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.2663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.2653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.2643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.2635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.2627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.2617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.2565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.2554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.2544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.2535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.2519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.2511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.2503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.2493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.2484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.2478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.2471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.2463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.2456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.2453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.2449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.2447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.2445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.2447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.2447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.2452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.2455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.2459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.2463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.2467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.2470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.2797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4258 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 642/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.1032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.6667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.7783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.8098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.8035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.9145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.9554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.9967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.0983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.1002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.1042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.1078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.1141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.1195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.1251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.1289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.1308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.1327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.1330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.1295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.1267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.1237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.1215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.1185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.1166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.1158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.1155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.1196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.1098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.1084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.1067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.1048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.1026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.1006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4283 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 643/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.8670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.8231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.3376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.9335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.0193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.7523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.2709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.2374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.2081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.1407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.1229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.1062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.9726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.9454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.9327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.8745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.8633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.8520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.8416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.8326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.8241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.8159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.8085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.7601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.7653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.7701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.7746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.7787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.7825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.7858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.7888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.7917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.7946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.7759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4309 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 644/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.4199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.2759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.1682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.8956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.8004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.5479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.3549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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3.2263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.2237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.2203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.2163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.2135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.2018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.1961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.1959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.1905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.1860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.1824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1814e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.1466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4327 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 645/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 3.9200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.7632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.9199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.8733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.7953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.7447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.6931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.6261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.5121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.4776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.4049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.3929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 3.4327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 3.4273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 3.4094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.4046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.3997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.3950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 3.3747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.3708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.3663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.3674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.3686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.3700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.3707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.3716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.3709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.3703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.3694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.3663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.3644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.3626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.3606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.3585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.3565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.3546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.3523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.3498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.3470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.3443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.3413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.3382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.3306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.3283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.3264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.3245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.3224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.3205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.3185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.3146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.3125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.3106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.3088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.3070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.3051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.3034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.3018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.2999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.2924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.2826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.2787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.2768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.2748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.2727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.2706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.2683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.2661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.2616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.2595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.2573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.2552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.2532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.2511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.2490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.2469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.2426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.2405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.2383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.2363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.2343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 2.9907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4348 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 646/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.4778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.7367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.8444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.8262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.8118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.8001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.7796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.6973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.6930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.6879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.6807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.6742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.6682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.6633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.6487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.6086e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.6022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.6032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.6043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.6057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.6061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.5990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.5979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.5969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4364 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 647/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.5549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.6724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.6057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.5118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.4675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.4597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.4410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.4424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.4391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4381 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 648/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.6975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.5901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.7737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.6892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.6550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.6275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.6047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.5221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.5025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.4260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.2991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.2969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.2949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.2928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.2914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.2894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.2264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4400 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 649/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.3509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.4843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.5127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.5081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.4807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.4497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.3798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.3435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.3328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.3141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.3132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.2952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.2889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.2551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.0844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.9620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.4328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.8461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.2090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.5269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.8060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.0650e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.2756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.2542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 9.2327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.2114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.1901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 9.1688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.1476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.1264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.1052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 9.0840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.0628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.0416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 9.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.9996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.9787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 8.9580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.9374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.8967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 8.8764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 8.8562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 8.8360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 8.8159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 8.7360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 6.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4374 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 650/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.8721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.1575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.9424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.9327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.9731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.9755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.9774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.9831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.9845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4384 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 651/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.1408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.2951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.2361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.1730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.1107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.0466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.8507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.8395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.8231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.8080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.7954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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2.7195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.6981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.6947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.6918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.6894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.6945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.6990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.7243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.7271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.7295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.7319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.7338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.7357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.7440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.7448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.7451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.7450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.7448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.7448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.7451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.7458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.7463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.7467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.7467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.7466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.7461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.7461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.7460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.7457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.7454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.7450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.7446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.7430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.7427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.7423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.7420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.7417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.7412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.7376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.7351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.7326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.7293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.7262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.7229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.7154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4401 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 652/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.3100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.2618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.2674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.2727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.2771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.2811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.2845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.2872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.3244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.3247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.3254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.3257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.3266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.3271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.3275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.3280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.3284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.3287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.3288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.3289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.3290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.3291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.3297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.3304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.3310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.3316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.3321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.3326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.3330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.3334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.3337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.3339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.3341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.3358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.3358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.3358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4421 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 653/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.2820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.1226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4441 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 654/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.7200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.9965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.0320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.9542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.9372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.9141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.9291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.9673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.0012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.8994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4459 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 655/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.7302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.8439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.8419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.8404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.8382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4480 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 656/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.5778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.6385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.6400e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.6542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.6556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.6574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.6589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.6603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.6619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.6634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.6646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.6657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.6669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.6681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.6689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.6696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.6702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.6710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.6718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.6726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.6733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.6741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.6746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.6751e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6788e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.6801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.6803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.6804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.6805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.6807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.6808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4500 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 657/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 1.2327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4516 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 658/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.1780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.5083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.6292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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1.4426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.4587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.4723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.4841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.4950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.5044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.5127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.5199e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.5992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.5992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.5991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.5990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.5978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.5975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.5972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.5968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.5965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.5962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.5958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.5954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.5950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.5946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.5941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.5936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.5932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.5929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.5925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.5920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.5916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.5913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.5909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.5905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.5900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.5896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.5891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.5870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.5249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4533 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 659/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 38s 322ms/step - accuracy: 1.0000 - loss: 1.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.3902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4551 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 660/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.7559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.7262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.6411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.6958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.6751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.8834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.3794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.5051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.5496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.5847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 [1m 25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.6677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.6681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.6661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.4903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.4762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.4196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.4054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.2949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.9829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.9441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.9161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.9069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.7737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.7511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.7275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4520 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 661/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.9385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.0012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.9968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.9856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.9614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.8779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4533 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 662/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.7475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.7011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.6631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.8456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.9225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.9757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.0974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.0941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.0906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.0866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.0816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.0479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.0163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.9785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.9754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.9365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.9280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.9094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.9010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.6429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4545 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 663/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.4216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.2534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.0221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.9451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.7924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.7534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.7315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.7262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.7209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.7164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4563 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 664/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.7808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.7835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.7847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.6211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.5621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.5196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.0488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.9597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.4260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.5634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.4559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.1140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1945e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.0443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.0280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.0227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.0174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.9698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.9202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.8713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.8241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.5951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.5508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.5071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.4640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.4213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.2963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.2555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.2151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.1751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.1356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.0966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.0582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.0203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.7317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.6973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.6632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.6294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.4979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.4658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.3714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.3104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.2507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.7202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4558 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 665/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.7756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.8905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.8838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.8613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.7761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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1.6870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.7533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.9577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.9965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.0919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.3595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.3642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.3686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.3725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.3763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.4031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.4067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.4086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.4099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.2032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4545 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 666/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.8705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.8238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.7835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.5998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.5937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.5881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.5836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.5794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.5743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.5660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.5665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.5678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.5690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4584 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 667/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.3208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.3138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.2853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.2762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.2632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.2619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.2633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.2637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4564 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 668/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.6988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.2805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.8183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4615 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 669/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.4850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.3757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.3482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.2812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.2682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.2578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.2468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.2394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.2334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.2314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.2178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.2139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.2107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 1.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.2067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.2071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.2068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 1.2071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.2075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.2080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 1.2080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.2077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.2071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 1.2068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.2059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.2047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.2040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 1.2035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.2028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.2021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 295ms/step - accuracy: 1.0000 - loss: 1.2016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.2010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.1998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.1992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.1990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.1987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.1983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.1983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.1982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.1981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.1980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.1977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.1973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 295ms/step - accuracy: 1.0000 - loss: 1.1970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.1967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.1964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.1960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 295ms/step - accuracy: 1.0000 - loss: 1.1958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.1959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.1958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 295ms/step - accuracy: 1.0000 - loss: 1.1956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.1955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.1952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.1950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 295ms/step - accuracy: 1.0000 - loss: 1.1948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.1946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 295ms/step - accuracy: 1.0000 - loss: 1.1943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.1943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.1943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 295ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 295ms/step - accuracy: 1.0000 - loss: 1.1943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.1941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.1938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 295ms/step - accuracy: 1.0000 - loss: 1.1936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.1934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.1932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 295ms/step - accuracy: 1.0000 - loss: 1.1930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.2049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.8210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4655 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 670/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.9737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 1.1035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.1300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.1514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.1532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4654 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 671/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.3148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4662 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 672/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.3607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.3034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.1011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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1.0535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.0517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.0498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.0441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.0431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.0422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.0413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.0403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.0392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.0383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.0341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.0020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.0009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.9992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.9891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.9813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.9738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.9664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.9600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.9539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.9475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.9409e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.9339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.9266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.8945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.8882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.8827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.8771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.8713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.8653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.8591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.8526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.8459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.8390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.8323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.8259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.8191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.8128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.8068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.8007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.7944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.7881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.7819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.7756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.7693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.7627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.7561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.7498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.7436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.7375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.7037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.6997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.6957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.6919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.2395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4672 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 673/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.6432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.9273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.6568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.6351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.5449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.3897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.2712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.1888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.1688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.1750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.2010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.2163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.2394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.2452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.2425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.2504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.2240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.2227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.2190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.2184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.2185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.2861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.2978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.3121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.3677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.4044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.4129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.4201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.4263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.4309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.4349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.4377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.4405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.4440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.4462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.4505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.4666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.4657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.4669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.4679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.4702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.4722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.4742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.4752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.4757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.4761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.4760e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.4752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.4736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.4720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.4703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.4687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.4680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.4673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.4679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.4679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.4675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.4678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.4680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.4707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.4709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.4710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.4711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.4706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.4670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.2363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4688 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 674/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.5813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.4731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.3799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.2870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.2251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.0504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.9444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.8638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.7914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.1704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.1626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.1569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.1514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 9.2170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4709 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 675/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.2666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.9456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.8490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.7618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.6766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.5911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.5099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.4335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.3643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.2990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 9.2373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.1838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.1346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 9.0858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 9.0370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.9891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.9422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 8.8978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.8528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.8118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 8.7739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.7368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 8.7037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.6733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.6446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.6152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.5863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.5625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.5391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.5163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.4954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.4748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.4554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.4358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.4175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.4018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.3874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.3729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.3579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.3435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.3288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.3138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2843e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.2575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.2454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.2339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.2225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.2115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.2004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.1890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.1778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.1666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.1552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.1437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.1326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.1218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.1114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.1013e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.0918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.0821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.0726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.0659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.0590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.0519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.0450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.0384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.0322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.0259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.0198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.0138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.0081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.0019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.9956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.9892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.9825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.9759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.9690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.9625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.9562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.9501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.9440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.9385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.9333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.9283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.9239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.9199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.9160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.9121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.9080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.9041e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.9004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.8966e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.8928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.8890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.8854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.8817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.8780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.8742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.8705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.8667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.8627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.8587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.8547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.8507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.3770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4721 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 676/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.0284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 6.0803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.6363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.7553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.7971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.8024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.7839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.7659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.7384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.7199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.7139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.7195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.7431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.7755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.8347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.8492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.8665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.8744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.8873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.8956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.9274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.9369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.9328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.9303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.9275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.9242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.9235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.9227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.9170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.9126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.9057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.9015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.9029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.9016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8994e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.9000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.9007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.9018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.9032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.9041e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.9046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9097e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.9235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.9239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.9246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.9252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.9255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.9257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.9239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.9237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.9236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.9233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.9231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.9227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.9221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.9213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.9203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.9192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.9182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.9171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.9163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.9154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.9145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.9134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.9123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.7401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4735 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 677/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.1375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.1842e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.5918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.6220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.5682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.4996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.5689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.6148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.6130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.6102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.6037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.6113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.6176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 6.6196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.6145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.6049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.5953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.5881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.5775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.5662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.5574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.5539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.5522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.5500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.5446e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.5426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.5476e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.5606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.5600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.5597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.5596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.5596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.5593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.5588e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.5506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.5510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.5516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.5522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.5528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.5528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.5527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.5541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.5554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.5562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.5569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.5576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.5585e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.5595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.5607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.5619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.5630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.5638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.5644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.5647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.5649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.5649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.5646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.5643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.5643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.5643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.5646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.5648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.5649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.5648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.5645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.5640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.5634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.5627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.5596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.4866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4751 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 678/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.1244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.5943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.8949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.9118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.8670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.8274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.8052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.7815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.7440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.7180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.7037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.6853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.6681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.6701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.6822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.7006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.7242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.7535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.7800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.8077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.8527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.8926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.9314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.9635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 5.9932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.0204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.0451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.0663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.0858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.1021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.1166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 6.1326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.1494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.4282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.6843e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.9167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.1325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.3311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.5149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.6838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.8390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.9833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.1166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.2391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.3513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.4561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.5549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.6455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.7299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.8092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.8842e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.9533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.0176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.0778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.1338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.1848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.2314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.2746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.3155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.3538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.3899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.4234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.4828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.5089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.5326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.5542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.5736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.5913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.6815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.6978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.7043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7096e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.7236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.7255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.7270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.7279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.7281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.7276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.7188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.7162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.7132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.7100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.7067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.7032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.6996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.6957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.6914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.6867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.6817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.6765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.6709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.6536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.6286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.6222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.6158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.6091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.6022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.5952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.5881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.5810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.7340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4773 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 679/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.8138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.4168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.2313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.9168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.6681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.4913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.2262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.1194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.0163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.9931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.9844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.0460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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6.0312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.0605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.0733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.0704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.0671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.0636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.0595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.0550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.0401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.0379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.0356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.0331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.0305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.0278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.0148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.0130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0096e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.9960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.9954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.9946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.9937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.9927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.9916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.9904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.9891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.9878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.9867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.9861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.9855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.9849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.9848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.9845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.9841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.9835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.9830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.9824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9807e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.9226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4784 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 680/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.5674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.9212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 9.7348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.3467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.0097e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.7028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.4409e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.1885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.9653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.8048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.6658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.5451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.4497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.3648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.2797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.2027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.1265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.0563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.9867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.9188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.8567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.8019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.7483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 6.7016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.6620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.6283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 6.5941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.5600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.5258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.4923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.4587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.4254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3973e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.3250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.3058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.2882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.2704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.2530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.2362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.2190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.2024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.1863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.1705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.1580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.1456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.1339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.1229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.1117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.1000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.0911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.0817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.0719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.0619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.0521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.0431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.0353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.0280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.0208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.0151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.0098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.0045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.9994e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.9946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.9895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.9840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.9781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.9732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.9687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.9640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.9593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.9548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.9506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.9463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.9417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.9369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.9321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.9271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.9220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.9173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.9041e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.9012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.8980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.8947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.8914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.8878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.8841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.8802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.8761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.8723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.8684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.8644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.8606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.8569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.8420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.8380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.8339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.8298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.8259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.8220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.8182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.8144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.8108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.8070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.8034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.7997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.7960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.7921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.7883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.7846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.7811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.7777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.3740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4793 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 681/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.4758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.3690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.1931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.1171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.0305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.8968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.8304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.8265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.8197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.8142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.8126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.8226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.8295e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.9288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.9749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.0518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.1454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.2281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.3900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.4584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.5235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.5840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.6397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.6903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.7372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.7810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.8201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.8575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.8915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.9222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.9495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.9737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.9955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.0159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.0338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.0507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.0666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.0826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 6.0966e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.1094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.1207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.1306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 6.1396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.1479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.1553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 6.1619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.1674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.1729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 6.1782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.1838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.1883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.1923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.1967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.2021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.2066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.2105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.2140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.2172e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.2199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.2232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2295e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2409e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.2398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.2393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.2386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.2377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.2268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.2247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.2228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.2209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.2188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.2169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.2150e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.2139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.2133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.2126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.2116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.2109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.2101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.2092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.2082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.2073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.2062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.0847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4810 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 682/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.8664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.2077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.6397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.6702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.6210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.5558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.5445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.5245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.4987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.4959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.5118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.6015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.6148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.6233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.6301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.6324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.6293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.6259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.6256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.6229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 4.6201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.6188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.6194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.6173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.6209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.6213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.6229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.6238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.6256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.6290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.6319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.6337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.6350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.6368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.6384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.6428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.6470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.6524e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.6579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.6620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.6663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.6712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.6756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.6792e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.6822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.6845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.6861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.6870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.6872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.6873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.6881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.6886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.6900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.6916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.6930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.6940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.6946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.6948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.6963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.6975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.6985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.7029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.7094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.7106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.7119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.7130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.7150e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.7159e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.7213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.7216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.7217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.7219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.7222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.7224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.7224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.7226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.7226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.7227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.7226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.7225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.7222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.7219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.7215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.7212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.7210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.7210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.7209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.7209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.7209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.7206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.7203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.7199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.6877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4821 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 683/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.3816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.3727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.9795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.5561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.2547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.0306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.8600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.7231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.5982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.5020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.4277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.3518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.2405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.1561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.1185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.0836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.0532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.0233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.9964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.9756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.9626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.9315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.9034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.9309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.9421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.9468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.9481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.9492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.9503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.9518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.9527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.9532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.9535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.9538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.9543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.9546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.9553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.9427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.9423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.9420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.9417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9409e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.9404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.9395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.9384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.8628e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4836 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 684/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.9363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.3805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.5777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.6016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.5814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.5456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.4863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.4991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.5088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.5202e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.5726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.6171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.6586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.6993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.7328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.7641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.7903e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.0982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1202e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.1308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.1354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.1421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.1676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.1708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.1738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.1764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.1789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.1809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.1825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.1840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.1851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.1862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.1872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.1892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.1911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.1926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.1939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.1948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.1954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.1957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.1959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.1961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.1959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.1958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.1956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.1954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.1951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.1945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.1900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.1885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.1871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.1855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.1838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.1821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.1804e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.1788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.1770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.1752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.1732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.1709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.1685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.1662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.1639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.1616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.1593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.1571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.1551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.1531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.1511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.1491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.1470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.1447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.1423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.1401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.1380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.1358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.8733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4849 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 685/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.2118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 9.8206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.0738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.5086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.0749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.7438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.4557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 7.2226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.0259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.7025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.2684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.1799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.1094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.0422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.8078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.7144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.6750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.6384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.6026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.4702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.4136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.2440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.2087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.1930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.1788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.1650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.1543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.1443e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.0969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.0887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.0802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.0718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.0646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.0571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.0500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.0433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.0368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.0301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.0233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.0169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.0106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.0043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.9977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.9735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.9575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.9428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.9230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.9181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.9136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.9091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.9051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.9011e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.8971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.8811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.8771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.8733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.8695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.8658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.8621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.8586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.8436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.8397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.8358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.8319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.8281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.8244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.8206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.8066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.8032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.7998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.7964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.7825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.3713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4861 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 686/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.6398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.0429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.0208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.7071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.5182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.4032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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4.3755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.3739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.3742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.3706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.3788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.3892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.3982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4443e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.4462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.4481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.4501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4570e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4570e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.4457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.4457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.4445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.4438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.4430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.4423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.4415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.4408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.4353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.4339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.4326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.4245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.4233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.4221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.2650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4873 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 687/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.9035e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.8359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.7802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.7099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6837e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.7436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.7646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.7704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.7728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.7755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.7807e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.7879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.7926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.7957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.7988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.8016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.7992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.7951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.7919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.7897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.7895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7889e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.7897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.7972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.7993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.8018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.8043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.8132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.8148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.8162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.8173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.8208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.0549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4885 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 688/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 7.3983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 6.7746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.3986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.1464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.9017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.9057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.8429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.7522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.6444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.5444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.4588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.3779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.3051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.2402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.1857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.1310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.0786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.0291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.9820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.9354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.7947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.7671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.7431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.6977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.6078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.5857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.5645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.5450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.5260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.5083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.4917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.4766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.4615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.4473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.4341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.4216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.4089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.3960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.2991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.2768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.2719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.2671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2267e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.1994e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.1981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.1968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.1938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.1829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.1809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.1790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.1770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.1754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.1745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.1696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.1684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.1672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.1660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.1649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.1639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.1630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.1623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.1615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.1606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.1595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.1585e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.1573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.1560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.1547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.1534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.1521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.9926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4898 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 689/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.6710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.1425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.3215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.2646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.2787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.3103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.3186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.3497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.3698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.3794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.3855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.3994e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.4023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.4040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4035e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.3990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.3970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.3953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.3997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.4264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.4296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.4323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.4350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.4375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.4401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.4426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.4457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.4491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.4523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.4550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.4576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.4601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.4625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.4644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.4660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.4676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.4694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.4709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.4729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.4749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.4770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.4788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.4806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.4821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.4834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.4919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.4942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.4965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.4987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.5006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.5025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5041e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.5090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.5104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.5120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.5134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.5148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4909 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 690/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 4.2687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.5428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.7119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.0012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.0960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.0397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.9347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.8112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.6902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.5877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.4981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.4086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.3335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.2612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.1939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.1266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.0626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.0070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.9529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.9014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.7626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.7232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.6858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.6506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.6167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.5840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.5526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.5225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.4926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.4635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.4347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.4071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.3818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.2922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.1832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.1228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.1102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.0984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.0878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.0775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.0674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.0577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.0483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.0389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.0300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.0221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.0155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.0091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.0031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.9977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.9922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.9868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.9815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.9293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.9254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.9216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.9175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.9136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.9098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.9062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.9026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.8962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.8874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.8758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.8673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.8596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8570e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.8492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.8411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.8338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.8250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.8187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.8106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.5880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4923 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 691/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.1286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.6342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.4038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.0639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.8641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 3.8371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.8365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.8345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.8357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.8351e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.8012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.7987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.7963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.7938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.7922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.7908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.7894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.7875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.7851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.7825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.7797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.7771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.7740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.7714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.7687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.7662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.7637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.7612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.7593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.7571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.7548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.7523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.7498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.7470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.7439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.7408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.7379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.7349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.7324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.7305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.7287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.7267e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.7246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.7222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.7197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.7169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.7142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.7114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.7087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.7062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.7036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.7012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.6989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.6966e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.6944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.6920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.6897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.6873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.6849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.6824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.6800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.6775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.6752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.6730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.6709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.6687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.6668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.6649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.6629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.6608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.6588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.6567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.6547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.6527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.6507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.6505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.6509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.6513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.6520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.6527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.6531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.6535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.6541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.6547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.6552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.6559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.7394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4936 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 692/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.7445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.1821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.0149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.8653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.5883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.6431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.6342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.6257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.6173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.6025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.5967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.5914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.5269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.5197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.5083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.5008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.4779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.4730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.4674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.4653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.4626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.4599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.4573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 3.4544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.4484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.4366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.4274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.4184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.4063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.4003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.3975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.3886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.3789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.3727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.3642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.3622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3336e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.2938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4949 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 693/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.7216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.6984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.6598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.3815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.3744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - 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3.2974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.2706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.2620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.2536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.2451e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.1444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.1334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.0992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.0974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.0958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.0975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.0988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.3715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4959 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 694/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.2064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.2455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.9299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.8947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8581e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.8028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.7884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.7754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.7318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.7140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.7280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.7392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.7467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.7518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.7546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.7549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.7527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.7492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.7475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.7459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.7433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.7428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.7422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 3.7375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 3.7279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.7199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.7138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 3.7101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.7079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.7054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.7023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 3.6962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.6929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.6895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.6861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.6840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.6815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.6760e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.6661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.6625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.6594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.6562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.6434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.6400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.6364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.6328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.6250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.6190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.6169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.6102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.6000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.5974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.5950e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.5925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.5901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.5877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.5853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.5828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.5803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.5778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.5752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.5725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.5697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.5670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.5644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.5617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.5593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.5568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.5544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.5519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.5494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.5471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.5446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.5421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.5320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.2347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4967 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 695/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 2.6841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.8372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.9123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.8340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.8201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.7707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7409e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.0148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.0868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.1464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.1713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.1940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.2177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.2781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.3092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.3223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.3340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.3447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.3542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.3636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.3722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.3806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.3880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.3947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.4205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.4200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.4193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.4186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.4148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.4116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.4098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.4074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.4045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.4016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.4006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.3971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.3932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3889e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.3875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.2198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4980 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 696/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.7000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.5099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.6053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.4403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.0677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.7602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.4987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.1053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.9555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.8238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.7171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.6241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.2435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.1245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.5875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3850e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.3421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.3246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.3104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3011e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.2687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.2622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.2533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.2460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.2392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4988 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 697/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.7631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.8187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.6881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4842e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.8521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.7893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.7852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.7813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.7755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.7703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.7685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.7663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.7646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.7632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.7616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.7601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.7545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.7527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.7508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.7485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.7463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.7440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.7418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.7364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.7357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.7348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.7337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.7327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.7315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.7303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.7291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.7280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.7269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.7258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.7251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.7244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.7506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.7756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.7996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.9061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.9250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.9434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.9620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.9801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.9975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.0911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.1051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.1186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.1319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.1451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.1580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.1707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.1830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.1949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.2064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.2175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.2282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.2386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.2489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.2588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.2689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.2788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.2886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.2981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.3072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.3162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.3248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.3332e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.3412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.3490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.3567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.3641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.2520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4993 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 698/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.4146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.9257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.8234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.3779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.0598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.0028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.8102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.7995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.7903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.7833e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.7322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.7281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.7235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.7188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.7136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.7081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.7022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.6893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.6844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.6804e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.6763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.6672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.6617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.6593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.6572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.6551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.6529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.6513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.6506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.6502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.6494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.6492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.6487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.6479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.6466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.6452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.6437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.6426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.6413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.6399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.6388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.6378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.6365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.6353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.6340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.6325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.6309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.3736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5005 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 699/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.0573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.0007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.8939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.3964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.3868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.3780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.3588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.3487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.3390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.2950e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.2740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.2525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.2481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.2311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.2287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.2283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.2366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.2382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.2396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.2424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.2450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.2470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.2486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.2501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.2518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.2531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.2547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.2562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.2576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.2587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.2598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.2606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.2612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.2630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.2651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.2669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.2670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.2659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.2646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.2641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.2629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.2624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2041e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5017 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 700/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.5029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.8341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.9163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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3.0607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.1670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.1707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.1742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.1764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.1777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.1783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.1783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.1696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.1623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.1560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.1492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.1466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.1321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.0931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.0805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.0716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0686e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.0625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.0493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0041e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.9999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.9978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.9956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.9935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.9913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.9891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.9869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.9848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.9827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.7278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5023 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 701/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.4958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.5904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.0531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.2895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.7652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.0106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.1959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.1764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.1546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.1299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.1022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7700e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.5982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.0728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5041 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 702/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.2552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.1890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.4726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.1662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.0562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.0515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.0473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.0429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.0382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.0328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.0273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.0219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.0169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.0125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.0083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.0044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.0003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.9349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.9308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.9268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.9230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.9191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.9152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.9115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.9089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.9063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.9036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.9008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5047 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 703/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.6404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.6019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.7133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.7191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.7001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.6942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.6763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.6447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.6319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.6249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.6114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.6012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.5063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.4531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.4428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4700e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.4705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.4702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4700e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.4695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.4435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5059 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 704/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.4143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.4363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.4055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.3763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.3504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.3300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.3309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.3173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.3074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.3148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.3166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3524e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.3644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.3646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3628e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.3534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.3487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.3514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.3538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.3545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.3550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.3545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.3532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.3520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.3508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.3482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.3463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.3453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.3451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.3449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.3447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.3445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.3444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.3442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.3441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.3441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.3440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.3438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.3436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.3433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.3430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.3426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.3421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.3417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.3412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.2868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5062 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 705/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.6960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.7908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.0259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.0559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.0523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.9944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.9751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.9941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.0900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.1411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.1386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.1396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.1405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.1413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.1435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.1451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.1464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.1473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.1480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.1481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.1478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.1471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.1464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.1455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.1454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.1454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.1458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.1462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.2010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5071 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 706/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.7676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.7742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.7186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.4386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.4261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.4354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.4412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 2.4444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 2.4463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 2.4472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 2.4470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 2.4452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 2.4426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 2.4398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.4375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.4344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.3989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.3953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.3919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.3886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.3779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.3673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.3531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.3441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.3360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.3277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.3171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.3097e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.3031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.3009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.2986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.2964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.2944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.2925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.2907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.2890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.2873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.2855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.2838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.2821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.2803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.2785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.2766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.2747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.2728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.2709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.2691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.2674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.2658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.2643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.2627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.2612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.2596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.2589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.2581e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.2578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.2576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.2573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.2570e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.2568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.2565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.2562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.2558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.2553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.2548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.2543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.2538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.2532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.2527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.2521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.1855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5081 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 707/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.4228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.0934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.1012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.1133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.1228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - 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2.1440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0837e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0823e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0686e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.0710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.1308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5091 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 708/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.3239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.7386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.8036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.7201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.6757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.6280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.5831e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.5667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.5430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.5136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.4707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.3623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.2307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.2002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0807e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0686e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0443e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.1974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5101 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 709/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.3635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.2763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0581e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0096e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9850e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.9773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5112 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 710/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.2019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.1902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.2578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1332e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0842e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.0684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.0600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.0534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.0482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.0420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.0383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.0343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.0290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.0246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.0216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.8572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5120 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 711/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.6676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.7191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.8415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.7646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.5786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.7672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.7045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.6898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.6726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.6536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5804e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.4975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4843e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1850e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1150e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5125 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 712/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.0345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.0286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.9198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.7476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 295ms/step - accuracy: 1.0000 - loss: 1.7520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.7517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.7513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 295ms/step - accuracy: 1.0000 - loss: 1.7505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.7499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 295ms/step - accuracy: 1.0000 - loss: 1.7493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.7474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 295ms/step - accuracy: 1.0000 - loss: 1.7464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 295ms/step - accuracy: 1.0000 - loss: 1.7427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 1.7349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 1.7331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 1.7300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.7278e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.7247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.7244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.7243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.7253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.7259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.7265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.7270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.7276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.7282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.7286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.7289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.7292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.7297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.7301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.7305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.7308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.7312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.7319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.7320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.7321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.7323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.7326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.7328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.7330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.7339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.7340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.7341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.7341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.7339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.7301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5140 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 713/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6570e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.6640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.6685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.6724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.6755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.7052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.7203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.7248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.7270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.7285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.7346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.7365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.7375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.7384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.7388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.7405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.7413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.7420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.7426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.7446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.7456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.7463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.7468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.7477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.7917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5144 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 714/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.4068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.1080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.0418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.8747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.7809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.7495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.6846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - 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1.6558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6352e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6032e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.5983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5949e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5889e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5154 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 715/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.7693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.4187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.1920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.9747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.6629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.5756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.3307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.2631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.7523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5162 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 716/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.2206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.8601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.4859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.2534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.0603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.9059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.7442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.6284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.5806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.5388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.5027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.3053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.2810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.2166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.0705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0295e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0011e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.8990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5169 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 717/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.9143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.9383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6096e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.6610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.6985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.7715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.9387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.9698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.5576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.5205 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 718/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.6401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.4322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.1746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.8716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.1337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.0139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 3.7410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.6726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.3416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.3053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.2240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.2003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.8907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.8767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.8598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.8471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.7975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.7824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.7788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.7752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.7717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.7684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.7650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.7616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.7479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.7446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.7414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.7381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.7349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.7320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.7292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.7263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.7234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.7204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.7178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.7150e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.7123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.7098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.7074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.7050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.7026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.7004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.6983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.6963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.6943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.6923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.6903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.6882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.6860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.6839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.6818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.6797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.4307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5220 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 719/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.2952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.0435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.8284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.6419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.2263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.2168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.2106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.2040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.1705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.1571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.1469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.0965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0700e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.0684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.0669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.0658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.0650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.0644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.0642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.0853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.0992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.6024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5230 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 720/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.9478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.0446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.0742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.1507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 2.1771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 2.1931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 2.1900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 2.1865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 2.1813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 2.1744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 2.1720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 2.1678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 2.1620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 2.1095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0096e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8686e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8581e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8524e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.7448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5227 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 721/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.7000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.6784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.5690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8097e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - 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1.6699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6157e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5700e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5655e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5238 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 722/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.2213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.1112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6267e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6011e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5994e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.5924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.5905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.5884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.5867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.5848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.5834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.5822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.5809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.5801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.5793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5241 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 723/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.3293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.5219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.9180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.0420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.0776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.0216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.0009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.9846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.9704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.9582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.9434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.9325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.9200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.9079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8950e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.8228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.8160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.8091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.8070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.8014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 1.7741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 1.7439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 1.7355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 1.7329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.7250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.7210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.7163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.7124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7013e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.6985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.4828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5250 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 724/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.4449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.4199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4041e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.6882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5257 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 725/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5172e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4950e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5257 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 726/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.4212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.4082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5270 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 727/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 1.3126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.5852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.6267e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3295e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3096e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3157e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.3803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5275 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 728/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - 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1.4004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3628e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3628e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5278 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 729/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.4401e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.4133e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.6556e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.5754e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.6665e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.6707e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.8094e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.8501e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.8299e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.8283e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.8561e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 9.8639e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9553e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3443e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.4991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5290 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 730/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4966e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2912e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.2862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.2850e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2837e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2792e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5292 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 731/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.4246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.9891e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.9643e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.9583e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.9505e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.9477e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.9458e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.9660e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.9792e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 9.9887e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.0001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.0010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.0017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.0171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.0178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.0183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.0192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.0201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.0210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.0253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.0260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.0266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.0271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.0275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.0281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.0287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.0308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.0326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.0350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.0367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.0378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.0384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.0404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.0417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.0431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.0448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.0460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.0481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.1106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.5295 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 732/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.2105e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.0094e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.4138e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.3418e-10 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.1163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.1381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.7085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.6148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.5717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.5115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.3055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.0979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.2719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.5542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.8118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.2623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.1797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.5952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.1693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.5898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.8578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.1157e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.1290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.8443e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.6592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.0064e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9758 - val_loss: 0.4857 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 733/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 0.9994 - loss: 0.0035 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 0.9994 - loss: 0.0032 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 0.9994 - loss: 0.0030 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0029 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 0.9994 - loss: 0.0028 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 0.9994 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 0.9994 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9994 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9994 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9994 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 0.9994 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4998  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9993 - loss: 0.0027 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9993 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9993 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 0.9993 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9993 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9993 - loss: 0.0026 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - 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━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9994 - loss: 0.0021 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9994 - loss: 0.0020 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0019 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.9995 - loss: 0.0018 - mean_absolute_error: 0.5000 - 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━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9995 - loss: 0.0017 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9995 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9996 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9996 - loss: 0.0016 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0015 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9996 - loss: 0.0014 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4997 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 5.5317e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9770 - val_loss: 0.3771 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 734/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.3299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.3760e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.3398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2259e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.1993e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1755e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1603e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.1465e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1344e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1276e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1200e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.1105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1013e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0913e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0600e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0502e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0421e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0342e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0273e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0215e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0095e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0035e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9973e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9910e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9667e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.9613e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9511e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.9463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9362e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9312e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9263e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8988e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8908e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8867e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8826e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8785e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8743e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.8634e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8611e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8565e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.8544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8522e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8500e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.8477e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8430e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8406e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8382e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8311e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8265e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8241e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8217e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8193e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8168e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8143e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8118e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8094e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8046e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8022e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7998e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7975e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7952e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7929e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7882e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7859e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7812e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7790e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7767e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7745e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7702e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7680e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7658e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7636e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7614e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7569e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7525e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7503e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7482e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7460e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7418e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7335e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7316e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7262e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3749 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 735/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2057e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1523e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1062e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0932e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0886e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0857e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0813e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0786e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0765e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0739e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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1.0602e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0588e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0554e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0518e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0492e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0480e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0470e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0461e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0422e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0408e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0393e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0378e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0363e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0324e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0302e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0290e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0279e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0269e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0260e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0250e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0221e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0212e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0204e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0189e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0181e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0172e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0164e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0155e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0146e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0129e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0121e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0105e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0097e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0082e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0066e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0059e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0033e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0025e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0009e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.9935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.9854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.9772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.9689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.9518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.9430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.9343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.9258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.9176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.9095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.9018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.8941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.8538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.8371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.8289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.8207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.8126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.8047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.7990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3785 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 736/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.4071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.0133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.7797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 8.6043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.4421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.2976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.1793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.0518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.8657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.8319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.7283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.6706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.6427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.6175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.5961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.4734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.4530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.3992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.3830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.3691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.3555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.2273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.2146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.2031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.1569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.1451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.1225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.1118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.1017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.0442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.0350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.0258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.0166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.0075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.8975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.8902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.8828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.8758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.8690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.8550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.8273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.8206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.8075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.8011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.7600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.7543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.7486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.1302e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3833 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 737/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.9307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.0375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.9164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.7452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.6189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.5348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.4663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.4217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.3686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 5.3557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.3363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.3282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.3282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.3282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.3196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.3106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.2985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 5.2866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.2057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.1996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.1931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.1728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.1606e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.1549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.1499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.1461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.1431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.1323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.1291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.1255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.1197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.1168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.1137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.1113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.1090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.1062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.1036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.0976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.0942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.0906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.0869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.0836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.0800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.0697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.0643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.0614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.0585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.0553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.0414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.0390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.0373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.0357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.0339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.0320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.0250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.0235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.0208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.0197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.0185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.0171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.0144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.0128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.0111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.0094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.0063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.0047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.0034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.0023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.0011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.9806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.9789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.9772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.7614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3877 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 738/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.2079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 304ms/step - accuracy: 1.0000 - loss: 4.5587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 4.6018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.5639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.5178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.4615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.4252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.3463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.2637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.2984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.2966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.2974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.2970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.2898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.2858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.2814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.2766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.2715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.2633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.2650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.2668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.2696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.2723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.2741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.2758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.2770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.2786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.2809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.2820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.2575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.2531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.2465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.2411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.2360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.2291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.9894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3893 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 739/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.1514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.3410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.2501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.0655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.9100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.8443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 3.6958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.6104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.5954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.5603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.5615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.5633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.5649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.5647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.5644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.5631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.5616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.5602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.5585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.5574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.5555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.5549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.5551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.5542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.5531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.5518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.5503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.5484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.5463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.5441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.5422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.5400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.5381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.5367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.5352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.5332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.5311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.5304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.5294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.5281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.5266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.5250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.5238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.5188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.5102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.5006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.4717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.4680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.4642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.4591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.4492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.2841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3931 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 740/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.0819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.2465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.1051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.0754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.0716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.0676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.0505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.0410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.0334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.0047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.8538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3957 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 741/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.7882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.7875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.7826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.7771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.7711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.7637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.7552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.7463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.7377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.7164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.7036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.6995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.7141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.7305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.7458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.7593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.7716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.7829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.7932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.8313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.8371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.8428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.8482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.8528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.8693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.8713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.8733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.8749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.8765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.8779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.8792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.8801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.8809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.8815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.8820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3973 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 742/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.7833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.7218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 2.6677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.4473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.4167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.4020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.2925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.2785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.2728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.2664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.2619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.2516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.2485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.2378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.1198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4002 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 743/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.8954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.7142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.1147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.0960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.0944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.0913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.0911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4027 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 744/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.1778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4051 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 745/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6509e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.6497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.6463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4073 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 746/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.8061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.5932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.5300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.5034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4089 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 747/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.5730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3547e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4110 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 748/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.3422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.3200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.2043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.2072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.2161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.2228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.2233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.2225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.2206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.2193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2172e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4128 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 749/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.4078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.1476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4145 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 750/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4160 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 751/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.9828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.9629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.9476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.9326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.8997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.8834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.8661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.8483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.8337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.8236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.8145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.8060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.7970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.7859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.7765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.7679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.7586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.7503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.7423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.7339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.7260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.7173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.7109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.7055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.6999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.6996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.6991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.6980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.6992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.6990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.6985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.6976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.6978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.6974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.6974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.6972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.6935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.6915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.6889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.6861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.6814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.6763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.6746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.6726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.6622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.6570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.6547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.6526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.6501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.6476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.6361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.6330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.6302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.6273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.6202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.6101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.5982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.5951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.5922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.5863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.5850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.5839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.5826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.5767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 9.4014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4180 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 752/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.9769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.8212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.6890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.5898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.5304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.5054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.4887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 9.4806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.4902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.4867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.4824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.4699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.4534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 9.4401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 9.4282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.4060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.4000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.3933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.3831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.3723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.3627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.3533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.3421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.3088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.3028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.2978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.2929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.2857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.2781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.2701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.2615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.2416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.2322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.2235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.2141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 9.2055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.1982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.1916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 9.1840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.1784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.1725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.1663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 9.1598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.1534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 9.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.1312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 9.1265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.1217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.1160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.1117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.1071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.1024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.0976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.0925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.0872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.0774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.0724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.0678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.0640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.0553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.0511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.0471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.0430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.0386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.0342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.0300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.0256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.0212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.0175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.0139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.0060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.0018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.9974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.9928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.9881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.9833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.9790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.9746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.9623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.9442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.9395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.9346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.9298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.9250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.9205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.9163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.9007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.8969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.8931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.8892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.8747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.4463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4190 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 753/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.1931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.8160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.6198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.4713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.3669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.2580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.1743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.1125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.0904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.0321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.0033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.9333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.9082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.8887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.8716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.8569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.8795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.8999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.9275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.9662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.2128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.2259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.2360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.2447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.2544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.2623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.2705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.2783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.2848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.2890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.2924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.2952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.2970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.2973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.2968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.2956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.2944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.2912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.2898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.2881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.2856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.2828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.2794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.2755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.2713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.2672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.2626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.2584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.2545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.2505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.2459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.2367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.2323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.2276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.2231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.2185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.2138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.2088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.2043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.1968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.1887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.1846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.1801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.1713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.1670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.1629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.1585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.1543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.1505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.1467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.1427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.1390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.1351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.1310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.1268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.1223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.1179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.1136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.1051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.1014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.0976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.0935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.0894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.0852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.0810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.0769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.0725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.0642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.0599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.5509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4209 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 754/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.4882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.9009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.7442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.6030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.3138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.2310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.1559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.0835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.0178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.9548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.8943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.8399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.7923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.7460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.7052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.6680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.6380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.6069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.5771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.5492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.5219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.4938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.4667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.4415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.3952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.3755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.3589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.3423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.3248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.3075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.2927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.2777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.2621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.2467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.2315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.2175e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.2041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.2031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.1989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.1846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.1812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.1772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.1730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.1685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.1641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.1601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.1564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.1531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.1497e-08 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.0664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.0628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.0594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.0560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.0488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.0450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.0372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.0332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.0294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.0254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.0217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.0038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.0001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.9962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.5243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4220 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 755/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.7550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.6613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.5731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.3003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.1485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.9823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.8454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.7117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.6093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.5232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.3995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.3438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.1158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.0384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.0246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.0142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.0059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.9837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.9424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.9183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.9038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.8981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.8927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.8877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.8827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.8772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.8716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.8662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.8618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.8570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.8529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.8497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.8468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.8430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.8389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.8345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.8249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.8107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.8022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.7922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.7891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.7859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.7825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.7792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.7756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.7724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.7698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.7675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.7656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.7639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.7624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.7606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.7588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.7570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.7553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.7537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.7519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.7502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.7486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.7470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.7454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.7442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.7431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.7416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.7401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.7316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.7299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.7283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.7199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.7189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.7179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.7171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.7147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.7136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.7121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.7100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.6542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4234 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 756/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 7.3105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.4512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.4160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.2802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.1278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.9585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.8555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.7751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.6907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.6204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.5739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 6.5277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 6.5028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.4511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - 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6.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.3194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.3144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.3112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.3069e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.2104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.2126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.2151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.2159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.2167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.2176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.2203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.2293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.2296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.2299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.2318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 6.3095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4251 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 757/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.6456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.7598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.9131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.9629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.8872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.8411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.7848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.7389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.6614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.6431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.6323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.6119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.5273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.5055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.4861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.4701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.4526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.4413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.4320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.4228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.4123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.4019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.3897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.3776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.3653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.3526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.3402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.3296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.3195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.3097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.3020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.2373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.2301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.2230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.2167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.2111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.2057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.1160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.1131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.1099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.1067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.1037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.1008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.0983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.0956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.0929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.0905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.2109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4258 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 758/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.6195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.5198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.4487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.5817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.5993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.5047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.3710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.2264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.0775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.9473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.8174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.7158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.6243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.5407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.4559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.3805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.3057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.2342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.1633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.1057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.9995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.9496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.9067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.8663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.7931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.7587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.7274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.6960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.6335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.6042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.5762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.5480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.5221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.4982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.4750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.4514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.4286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.4059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.3839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.3614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.3400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.2998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.2818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.2648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.2484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.2325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.2158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.1991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.1516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.1360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.1204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.1053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.0905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.0765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.0631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.0503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.0371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.0239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.0160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.0080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 6.9913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.9831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.9757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 6.9686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 6.9422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.9227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.9026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.8961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.8895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.8832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 6.8771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.8711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.8648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 6.8586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 6.8344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.8286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.8229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 6.8171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 6.8020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.7970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.7920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.7868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 6.7817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.7765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.7713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 6.7663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.7615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.7565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 6.7518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.7472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.7427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.7380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 6.7332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.7283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.7234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 6.7185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.7138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.7091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.7045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.6999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.1537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4262 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 759/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.5899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.5427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.4897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 6.2866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.1340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.9884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.9178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.8462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.7812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.7339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.7224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.7120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.7090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.7126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.7207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.7178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.7133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 5.7185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 5.7200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.7172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.7112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.7048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.7004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.6961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.6919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.6883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.6843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.6783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.6718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.6730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.6732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.6735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.6745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.6754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.6769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.6767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.6773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.6786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.6813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.6825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.6828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 5.6832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.6831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.6819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.6800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.6776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.6751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.6719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.6698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.6685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.6673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.6654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.6549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.6516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.6482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.6450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.6418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.6389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.6363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.6338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.6309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.6280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.6250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.6217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.6183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.6148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.6113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.6081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.6049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.6020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.5935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.5846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.5816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.5784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.5753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.5636e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.5416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.5409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.5403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.5397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.5370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.5362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.5354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.5351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.5348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.5343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.5340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.5320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.5312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.5304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.5295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.5258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.4148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4274 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 760/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.3472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.3396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.7024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.1741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.8518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.6104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.2316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.0860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.9710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.8882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.8111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.7526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.7107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.6357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.5681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.5389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.5105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.4829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.4574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.4348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.4173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.4076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.3998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.3830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.3735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.3648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.3560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.3466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.3030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.2227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.2211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.2189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.2151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.2132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.2116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.2096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.2091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.2077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.2060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.2033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.2020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.2002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.1996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.1264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4299 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 761/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.9912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.7605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.5990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.3945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.2494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.2037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.9838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.8969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.8848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.8737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.8037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.7971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.7939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.8098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.8049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.8014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.8005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.7871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.7831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.7816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.7789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.7768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.7772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.7764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.7756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.7734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.7716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.7702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.7683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.7664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.7642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.7616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.7609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.7602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.7596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.7568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.6701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4312 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 762/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.8647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.7806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.7327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.6217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.5325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.4562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.4329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.4013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.3633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.3761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.4169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.5919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.6680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.7360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.1094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.1418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.1712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.1978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.2220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.2445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.2651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.2865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.3066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.3253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.3426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.3584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.3730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.3865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.3986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.4097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.4197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.4288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.4373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.4447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.4517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.4641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.4692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.4737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.4776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.4811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.4841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.4866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.4962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.4980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.4991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.4999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.5002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.5003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.5002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.4839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.4816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.4792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.4767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.4744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.4722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.4624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.4597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.4570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.4542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.4515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.4487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.0817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4325 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 763/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.3305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.6796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.7566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.6774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.6667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.6613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.6553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.6679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.6735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.6703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.6660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.6489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.6365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.6226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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4.5636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.5374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.5329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.5289e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.5013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.4665e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.4561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.4552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.4542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.4533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.4523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.4515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.4508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.4500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.4491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.4481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.4470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.4459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.4450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.4439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.4429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.4419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.4409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.4400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.4392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.4384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.4363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.4357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.4350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.4343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.4336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.4329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.4338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.4375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.4381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.4388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.4393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.4701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4342 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 764/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.7536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.7706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.7467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.6699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.6043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.4971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.4461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.3941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.3891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.3842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.5287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.6731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.7930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.0556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.1743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.4130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.6330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.6715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.7058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.7367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.7630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.7879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.8107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.8322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.6539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.6311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.6135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.6019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.5735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.8709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4359 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 765/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.0233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.8501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.7297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.5346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.4347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.9982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.1048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.1225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.1438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.1489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.1535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.1577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.1626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.1672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.1713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.1751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.1787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.1819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.1851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.1882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.1913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.1940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.1967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.1992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.2014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.2034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.2052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.2068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.2084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.2100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.2118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.2135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.2151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.2163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.2175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.2186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.2196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.2203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.2209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.2216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.2225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.2235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.2244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.2253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.2262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.2268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.2275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.2287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.2292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.2295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.2297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.2293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.2276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4363 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 766/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.9256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.7378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.4796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.2941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.1671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.0697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.9854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.9123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.8506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.8102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.7733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.6829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.6747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.6653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.6551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.6453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.6657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.6822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.6977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.7122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.7268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.7410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.7540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.7645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.7739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.7813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.7869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.7972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.8237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4380 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 767/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.6879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.5612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.3912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.1695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.7078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.5207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.4505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.4491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.1388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.3134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.6103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.7377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.8538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.1462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.2276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.3012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.3684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.4292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.4846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.5347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.5799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.6214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.6938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.7255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.7547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.7814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.8473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.8650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.8807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.8944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.1093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4390 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 768/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.9547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.9058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.7639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.6600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.5741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.5041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.4196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.3932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.3818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.3757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.3746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.3547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.3546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.3532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.3517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.3502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.3488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.3471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.3445e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.3305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.3307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.3321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.3329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.3333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.3335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.3340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.3346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.3354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.3359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.3361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.3326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.3316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.3319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.3331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.3342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.3346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.3356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.3352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.3338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.3326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.3312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.3294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.3277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.3162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.3136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.2367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4412 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 769/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.9426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.8911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.7750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.6418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.4923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.4268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.4098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.3982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.3873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.3751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.3291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.3177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.3191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.3183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.3019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.2973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.2932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.2848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.2820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.2814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.2810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.3038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.3253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.3459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.3653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.3839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.0818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4356 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 770/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.2788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.4323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.5928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.7041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.6736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.6371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.6163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.5520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.5342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.5099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.4334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.4073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.3888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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4.2842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.2416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.2309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.2223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.2132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.2050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.1830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.1360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.1143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.1018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.0954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.0893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.0837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.0723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.0663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.0608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.0585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.0532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.0536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.0538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.0539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.0536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.0534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.0517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.0508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4394 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 771/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.0701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.4028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.0424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.6171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.3759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.2884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.2150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.7939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.4814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 9.1948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.9376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.7030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.4871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.2867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.1011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.9329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.6321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.4973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.3755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.1514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.0480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 6.9499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 6.8563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 6.7682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.6836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 6.5277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.4553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.3870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.3220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.2602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.2012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.1447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.0904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.0379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.9872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 5.9382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.8917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.8473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.8046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 5.7633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.7237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.6854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 5.6479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.6113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.5760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 5.5423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.5096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.4779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.4473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 5.4176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.3889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.3612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.3343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.3082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.2823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 5.2573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.2327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.2086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.1851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.1621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 5.1396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.1182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.0973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.0769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.0378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 5.0188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 5.0001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.9817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.9637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.9460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.9287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.9116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.8950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.8787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.8631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.8478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.8330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.8183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 4.8038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.7900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.7764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.7631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 4.7500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 4.7371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.7000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.6881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 4.6764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.6648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.6532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.6418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 4.6306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.6194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.6084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.5974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.5867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.5761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.5660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.5560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.5462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.5364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.5267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.5170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.5074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.4979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.3892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4422 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 772/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.6943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 3.5446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.4821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.0150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.0066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.0030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.2004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.2017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4438 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 773/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.3691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.2266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.0965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.4509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.4024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.2413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.2264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.1690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.1575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.1243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.1120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.1027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.0943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.0841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.0746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.0772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.0783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.0799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.0787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.0773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.0764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.0750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.0724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.0682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.8149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4446 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 774/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.8182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.7381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.7286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.7217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.7098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.7052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.6993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.6692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.6462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.6441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.6414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.6318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.6251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4459 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 775/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.8983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.2349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.1316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.9806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.9199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.7916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.7607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.7444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.7294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.7159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.7021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.7007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.6995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.6990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.6979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.6965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.6958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.6950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.6928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.6899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.6865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.6826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.6783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.6737e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.6199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.6165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.6134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.6108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.6079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.6048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.6015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.5950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.5916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.5881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.5850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.5819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.5790e-08 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.6045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.6082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 2.6117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.6151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.6183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.6214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 2.6243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.6271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 2.6297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.6322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.6395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.6483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.6543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.6615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.8762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4550 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 776/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.6586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.7447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.7144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.6379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.6072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.5331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.5249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.5186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.6072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.6163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.6159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.6155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.6140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.6127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.6104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.6091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.6083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.6037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.6011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.6003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4487 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 777/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.2968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.8383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.8148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.5934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.5786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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2.5273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.5020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4943e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.4362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.4140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4493 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 778/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.0965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.5522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.2423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.8685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.8133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.7913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.7545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.7108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.6613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.3933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.3870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.3816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.3750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.3703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.3658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.3600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.3556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.3501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.1907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4507 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 779/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.8937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.9254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.0364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.0625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.0679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.0666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.0569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.0486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.0443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.0363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.0282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.0218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.0160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.0112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.0068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.0043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.0011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.9897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.9882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.9870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.9862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.9848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.9834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.9821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.9805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.9791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.9785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4519 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 780/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.8416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.8019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.8128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4536 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 781/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.6915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.6920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.6922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.6923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.6946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4552 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 782/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.0206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.7413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.7398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.7383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.7368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.7353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.7338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.7284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.7249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.7217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.7181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.7172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.7164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.7156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.7149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.7142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.7135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.7105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.7098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.7091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.7085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.7078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.7072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.7066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.7061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.7055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.7049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.7042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.7036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.7030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.7025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.7029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.7033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.7037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.7041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.7495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4568 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 783/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.6636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.6173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.5998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.5981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.5964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.5949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.5933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.5919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.5907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.5895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.5882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.5870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.5848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.5837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.5826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.5816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.5809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.5802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.5796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.5793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.5790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.5787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.5783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.5780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.5778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.5775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.5770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.5758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.5748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.5738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.5720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.5781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.5814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.5851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.5873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.5896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.5926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.5945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.5966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4547 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 784/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.1104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.6998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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1.6692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.6510e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.7273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4565 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 785/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.6224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.5669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.6581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.6994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.7200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.7202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.7256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.7373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.7136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.7031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.6980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.6936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.6895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.6847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.6797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.6646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.6516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.6476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.6438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.6406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.6374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.6340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.6305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.6304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.6294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4601 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 786/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.4654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.5001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.5049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.5053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.5052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.5020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.5011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.5000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.4705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.4589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.4568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.4537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.4517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4620 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 787/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.2831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.1516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.0460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.7900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.7457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.7081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.6773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.6487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.6274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.6129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.5625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.3142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4628 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 788/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.1921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.9646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.7002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4640 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 789/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.8088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.3924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.2932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.2892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.2869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.2845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.2808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.2632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.2611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.2085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4656 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 790/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1784e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4665 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 791/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.2338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.3596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.4188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - 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1.2098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.1885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.1821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.1229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.1219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.1210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.1196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.1184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━[0m 3s 297ms/step - accuracy: 1.0000 - loss: 1.1181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.1175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.1164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.1154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.1141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.0760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4675 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 792/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.3936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.3393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.2884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.2433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.2055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.0795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.0766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.0742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.0720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.0700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4685 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 793/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.3041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.0382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4681 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 794/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.2415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.2235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.2058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.1903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.1382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4704 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 795/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.1073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.1027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.0154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.0140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.0110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.0024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.0011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.9992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.9871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.9384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.9268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.9158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.9052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.8952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.8849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.8747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.8644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.8541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.8437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.8333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.8234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.8141e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.8049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.8023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.7998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.7974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.7945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.7917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.7887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.7856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.7822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.7787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.7749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.7718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.7687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.7656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.7626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.7594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.7500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.7395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.7880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.8355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.8816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.5362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4688 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 796/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.1214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.9633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.2201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.2744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.3858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.3983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.4004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4692 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 797/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.3716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.4323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.3942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.3328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.3336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.2970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.2917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.2799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.2736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4717 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 798/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.9490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.9697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9267e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.9104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.9095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.9001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.9151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.9398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.9525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.9583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.9611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.9646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.9691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.9799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.9930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.9995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.9942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.9880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.9834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.9792e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.9746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.9691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.9648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.9633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.9621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.9591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.9550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.9503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.9462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.9418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.9393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.9375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.9351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.9317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.9284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.9256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.9227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.9191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.9150e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.9116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.9091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.9067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.9061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.9058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.9053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.9039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.9023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.9003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.8979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.8949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.8915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.8880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.8848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.8812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.8777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.8748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.8718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.8683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.8644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.8603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.8559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.8511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.8460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.8408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.8359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.8306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.8260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.8216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.8177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.8002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.7955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.7909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.7865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.7823e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.7779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.7738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.2383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4725 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 799/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.9819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.8365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.7310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.6425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.5705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.5149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.4068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.3556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.3083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.2805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.2624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.2375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.2146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.1948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.1741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.1549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.1425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.8425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.8525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.8567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8686e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.8719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.8721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.8727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.8750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.8768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.8784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.8836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8842e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.8855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.8895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.8977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.8992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.9017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.9058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.0271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4749 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 800/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.8603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.3515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.9457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.7265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.5427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.3758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.2603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.2952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.3217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.3285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.3360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.3371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.3407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.3392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.3316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.3248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.3201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.3133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.3074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.3029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.3004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.2775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.2662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.2540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.2410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.2289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.2206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.2124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.2109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.2138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.2158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.2157e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.2139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.2123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.2098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.2061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.2017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.1979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.1985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.2017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.2043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.2134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.2149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.2149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.2146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.2144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2035e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.2014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.2005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.2001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.1991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.1982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.1973e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.1975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.1971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.1964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.1958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.1955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.1948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.1942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.1937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.1932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.1925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.1915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1889e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.1834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.1817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.1798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.1785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.1773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.1763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.1714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.1705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.1695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.1687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.1676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.1665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.1663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.1662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.1662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.1661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.1657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.1654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.1648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.1642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.1635e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.1630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.1624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.0972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4756 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 801/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.8646e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.7106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 9.5632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.4477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.3491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.2604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.1767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.0989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.0299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.9699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.9170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.8604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.8157e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.7746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.7376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.7088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.6099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.5849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.5602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.5361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.5133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.4902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.4705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.4506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.4330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.4184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.4080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.3959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.3840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 8.3757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.3680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.3589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 8.3487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.3100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.3014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.2925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.2826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.2723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.2609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.2502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.2088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 8.1994e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 8.1918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 8.1844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.1777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.1702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.1627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 8.1548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.1472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.1392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 8.1310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.1227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.1176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 8.1123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.1074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.1024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.0979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 8.0930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.0883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.0832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 8.0784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.0733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.0683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 8.0634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 8.0587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 8.0538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 8.0492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 8.0449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 8.0408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 8.0365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 8.0321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 8.0281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 8.0244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 8.0209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 8.0172e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.0134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.0100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 8.0067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.0034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 8.0004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 7.9973e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 7.9939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 7.9903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 7.9866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 7.9828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 7.9787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 7.9744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 7.9701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 7.9659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 7.9617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 7.9577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.9540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.9503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.9463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.9423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.9382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.9342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.9302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.9144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.4725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9774 - val_loss: 0.4768 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 802/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 9.5137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.5232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.1956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.8627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.5858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.3711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.2065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.0839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.0184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.9298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8837e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8831e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.8814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.8671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.8515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.8339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.8173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.7967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.7728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.7604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.6702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.6616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.6548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.6529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.6511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.6505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.6508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.6573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.6615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.6653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.6678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.6687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.6693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.6703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.6738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.6758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.6782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.6811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.6849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.6899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.6937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.6964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.6991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.7009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.7020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.7030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.7036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.7032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.7036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.7042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.7045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.7038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.7026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.7010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.6991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.6969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.6943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.6915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.6894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.6870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.6845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.6824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.6806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.6782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.6766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.6748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.6733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.6714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.6690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.6663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.6639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.6614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.6591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.6573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.6407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.6396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.6386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.6375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.6365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.6356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.6344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.6330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.6316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.6299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.6284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.6269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.6255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.6244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.6233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.6220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.6210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.6199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.6185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.6172e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.6158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.6147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.6134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.6118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.6104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.6090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.6076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.4362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4778 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 803/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.7020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.4744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.2504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.9057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.7715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.6548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.5578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.4748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.3886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.3104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.2335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.1602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.0894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.0196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.9592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.9071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.8658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.8268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.7901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.7211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.6878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.6113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.5880e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.7660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.7702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.7728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.7749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.7775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.7793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.7813e-09 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.7817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.7798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.7779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.7711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.7648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.7588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.6173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4792 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 804/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.5933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.2158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.9200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.6624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.4803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.3595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.2410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.1380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.0574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.0102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.9564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.9201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.8466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.8122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.7754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.7400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.7099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.6807e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.6547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.6324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.6107e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.5858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.5969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.6034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.6070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.6091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.6090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.6085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.6083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.6071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.6057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.6040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.6013e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.5697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.5604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.5518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.5437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.5352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.5264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.5189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.5126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.5054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.4979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.4739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.4501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.3196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.3130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.2995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.2930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.2862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.2796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.2732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.2669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.2608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.2550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.2492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.2431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.2371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.2313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.2258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.2203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.2145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.2089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.2033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.1981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.1940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.1900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.1861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.1820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.1781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.1741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1843e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.1848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.1870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.2550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4793 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 805/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.1903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 6.3111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.1531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.0075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.0047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.0116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.1902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.2761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.3289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.5074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.5266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.5353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.6077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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1.5552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.3266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.6769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.5259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1256e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.7711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.1971e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.6970e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2175e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4834e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7549e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0537e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.3835e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.7320e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1286e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.5664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.0281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.5924e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1824e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.8001e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.4182e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.0555e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.6998e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.3632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.0502e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7469e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0466e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1202e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1927e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2665e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3426e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4177e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4913e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.5636e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6343e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7041e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7728e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 0.9999 - loss: 1.8397e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 1.9054e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 1.9701e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 2.0330e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 0.9999 - loss: 2.0942e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 2.1543e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 2.2126e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 0.9999 - loss: 2.2687e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 2.3230e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 2.3756e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 0.9999 - loss: 2.4264e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.4754e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.5229e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.5689e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 0.9999 - loss: 2.6135e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 2.6569e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 2.6990e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 0.9999 - loss: 2.7399e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 2.7795e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 2.8178e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 0.9999 - loss: 2.8546e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.8902e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.9246e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.9579e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 0.9999 - loss: 2.9901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 3.0211e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 3.0512e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 0.9999 - loss: 3.0803e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 3.1082e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 3.1351e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 0.9999 - loss: 3.1611e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 3.1861e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 3.2102e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 3.2334e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 0.9999 - loss: 3.2558e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 3.2773e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 3.2980e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 0.9999 - loss: 3.3180e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 3.3372e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 3.3556e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 0.9999 - loss: 3.3732e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 3.3902e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 3.4066e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 3.4224e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 0.9999 - loss: 3.4375e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 3.4521e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 3.4660e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 0.9999 - loss: 3.4793e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 3.4920e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 3.5042e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 3.5159e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 0.9999 - loss: 3.5271e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 0.9998 - loss: 4.8620e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9770 - val_loss: 0.3657 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 806/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.9269e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.1156e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.2994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.1517e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.9315e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7750e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.6005e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.4241e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2532e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1024e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.9666e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8556e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7527e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.6574e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5719e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.4908e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4137e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.3554e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.2982e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.2425e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1879e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.1367e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0869e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0385e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9916e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9462e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9021e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8592e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.8176e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7774e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7384e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.7006e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6638e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5936e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5601e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5277e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4963e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4659e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4363e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4075e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3795e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3523e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3258e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3000e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2748e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2503e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2265e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.2032e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1806e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1586e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1371e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1162e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0956e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0756e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0560e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.0369e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.0181e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.9998e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9819e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.9471e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9303e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.9139e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8977e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8819e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8664e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8513e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8364e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8217e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8074e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.7933e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7795e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7660e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7527e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.7397e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7269e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7142e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.7019e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6897e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6777e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.6660e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6544e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6430e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6319e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.6209e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.6100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.5994e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.5889e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5786e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5685e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5585e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.5486e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.5389e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.5293e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.5199e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.5107e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.5015e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4925e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4836e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4749e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4663e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4578e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4494e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4411e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4330e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4249e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4170e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4091e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4014e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3938e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3863e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3789e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3715e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3643e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3571e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3501e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3431e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3294e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.2053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3751 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 807/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0076e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.1201e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1308e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.1229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0968e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0804e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0625e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0478e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0360e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0244e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0144e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.8864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.7702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.7175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.6636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.6150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.5701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.5257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.4858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.4513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.3941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.3675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.3411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.3155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.2883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.2603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.2100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.1947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.1435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.0938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.0865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.9034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.8940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.8847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.8759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.8670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.8577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.8482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.8386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.8294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.8199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.8102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.8004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.7908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.7809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.7713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.7618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.7524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.7430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.7337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.7156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.7063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.6332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.6243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.6153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.6063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.4902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.4012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3805 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 808/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.3144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.2263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.0376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.9755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.8593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.7159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.5770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.4354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.3051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.1898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.9266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.7257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.5062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.4685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.4318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.3999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.3730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.3475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.3231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.1831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.1624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.1418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.1210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.1114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.1012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.0910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.0805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.0697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.0584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.0468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.0357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.0251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.0156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.0149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.0135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.0117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.0093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.0061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.0025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.9988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.9954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.9918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.9881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.9847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.9817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.9782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.9744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.9702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.9656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.9608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.9556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.9504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.9454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.9352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.9311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.9270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.9227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.9182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.9135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.9086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.9036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.8828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.8777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.8728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.8680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.8629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.8578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.8526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.8473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.8419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.8363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.8307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.8251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.8197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.8143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.8093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.8043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.7992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.7940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.7887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.7835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.7782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.7729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.7677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.7625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.7573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.7521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.7316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.7162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.7013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.1224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3859 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 809/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.3626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.6900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.6733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.5292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.4139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.5347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 4.7374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.8871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.9909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 5.0695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 5.1367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 5.1885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 5.2212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 5.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 5.2583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 5.2653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 5.2675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.2578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.2279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.2171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.2060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.1942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.1828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.1710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.1586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.1460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.1340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.1213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.1091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.0876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.0433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.9967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.9857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.9750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.9364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.9270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.9179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.9087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.8993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.8900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.8815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.8729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.8644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.8487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.8409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.8257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.8180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.8101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.8022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.7943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.7866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.7790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.7716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.7645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.7574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.7503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.7432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.7360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.7291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.7151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.7083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.7016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.6950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.6884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 4.6823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.6762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.6700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.6639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 4.6391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.5977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.5922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.5866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.5810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.5757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.5705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.5653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.5602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.5551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.5500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.5450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.5401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.5352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.5303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.5254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.5205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.5156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.5108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.5059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.5012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.4965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.9335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3891 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 810/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.9034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.1166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.2252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.1579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6240e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.3915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.3672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.3603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.3554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.3508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.3301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.3248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.3193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.3041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.2993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.2831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.2711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.2591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.2451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1873e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.1493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.1449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.1395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.9641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3928 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 811/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.6545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.8309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.9495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.7776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.7390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.6793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.6654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.6607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.6493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.6413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.6416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.6389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.6370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.6322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.6289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.6259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.6221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.6151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.6134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.6100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.6075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.6059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.6014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.5995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.5936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.5886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.5827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.5770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.5542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.5513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.5478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.5439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.5406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.5375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.5337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5326e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.5306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.5264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.4073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3962 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 812/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.2444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.3057e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.2288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.2246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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2.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1982e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.2006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.2002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1988e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.1698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.0748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3990 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 813/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.0793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.0784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.0085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.8688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.8756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.8834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.8879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.8899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.8887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.8872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.8870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.8867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.8847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8838e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.8830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.8808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.8801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.8784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.8749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.8739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.8730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8719e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.8717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.8700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.8679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.8673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.8662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.8651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.8640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.8627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.8614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.8595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.8013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4016 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 814/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.1292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.9129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 301ms/step - accuracy: 1.0000 - loss: 1.7496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.7468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.7443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 301ms/step - accuracy: 1.0000 - loss: 1.7417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.7394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.7334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.7314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 301ms/step - accuracy: 1.0000 - loss: 1.7271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.7247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.7222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 301ms/step - accuracy: 1.0000 - loss: 1.7198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.7176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.7154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 301ms/step - accuracy: 1.0000 - loss: 1.7134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 1.7102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 1.7086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 301ms/step - accuracy: 1.0000 - loss: 1.7068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 1.7050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 1.7031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 301ms/step - accuracy: 1.0000 - loss: 1.7011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 1.6992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 1.6973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 301ms/step - accuracy: 1.0000 - loss: 1.6941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 1.6926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 1.0000 - loss: 1.6913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 1.6875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 301ms/step - accuracy: 1.0000 - loss: 1.6861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 1.6847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 1.6839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 1.6830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 301ms/step - accuracy: 1.0000 - loss: 1.6822e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 1.6814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 1.6807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 301ms/step - accuracy: 1.0000 - loss: 1.6800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 1.6794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 1.6789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 301ms/step - accuracy: 1.0000 - loss: 1.6783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 301ms/step - accuracy: 1.0000 - loss: 1.6777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.6716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.6700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6628e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4037 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 815/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.7877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.8894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.8910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.8196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.7888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.7014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.6832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.6427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6061e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.5724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.4859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.4830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.4790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4722e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4566e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4062 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 816/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.5408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.5084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.4619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4237e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.4390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.4404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.4425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4488e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.4485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.4489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.4498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.4487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.4449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.4427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4394e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.4228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 1.4205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.4171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.4145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.4043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4029e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.4015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 1.3073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4078 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 817/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.4462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.1206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.7450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.0111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.1303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.0472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.9858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.9182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.8512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.7214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.6578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.5351e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.4769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.4211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.2673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.2212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.1774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.0971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.0589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.0221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8875e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.6524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.6089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.5315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.5137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.4965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.4798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.3621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.3238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.3000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.2771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.2443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.2236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.2136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.2038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.0916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.0693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.0411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.9776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.9604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.9439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.9231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.9082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.9034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8986e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.3329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4099 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 818/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1478e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1450e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1362e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.0961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.0941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.0920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.0890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.0871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.0855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.0834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.0820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.0807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.0761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.0748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.0738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.0723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.0274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4122 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 819/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0854e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.0382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - 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9.9515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.9424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.9399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.9349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.9268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.9213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.9191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.9166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.9163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.9176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.9194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.9181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.9160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.9128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.9086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.9028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.8955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.8883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.8817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.8744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.8677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.8619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.8568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.8508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.8449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.8384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.8320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.8248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.8169e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.8284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.8304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.8329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.8373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.8385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.8392e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.8446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.8459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.8466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.8468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.8466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.8462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.8458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.8449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.8443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.8440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.8434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.8428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.8424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.8419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.8411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.8401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.8389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.8375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.8362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.8346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.8320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.8305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.8291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.8233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.8220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.8206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 9.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4141 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 820/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.5462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.3572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.3131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.2731e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.2417e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.2241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.2073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 1.1939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.1633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.1533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 1.1249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.1160e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 1.1081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.0898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 1.0858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.0820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 1.0778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 301ms/step - accuracy: 1.0000 - loss: 1.0442e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0419e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.9888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.9743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.9606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.9463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.9316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.9168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.9021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.8871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.8730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.8594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.8462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.8209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.8094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.7986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.7874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.7378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.7283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.7194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.7103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.6930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.6848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.6762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.6676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.6589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.6501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.6416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.6328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.6240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.6070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.5905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.5828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.5751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.5675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.5597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.5521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.5444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.5366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.5289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.5214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.5138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.5066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.4997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.4930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.4861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.4792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.4722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4158 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 821/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.2548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.7889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.5801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.2928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.1135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.9482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.8989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.8484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.8034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.7593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.7164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.6766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.6417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.6062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.5741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.5492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.5274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.5036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.4803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.4017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.3666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.3496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.3348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.3251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.3167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.3064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.2957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.2736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.2503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.2388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.2289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.2188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.2095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.2008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.1925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.1763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.1687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.1613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.1535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.1388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.1090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.1031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.0996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.0956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.0915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.0869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.0821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.0645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.0257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.0229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.0182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.0156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.0128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.9969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.9937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.9931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.9925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.9921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.9921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.9918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.9914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.9908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.9900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.9890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.9880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.9870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.9861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.9850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.9850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.9854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.9852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.9849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.9844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.9818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.9066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4170 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 822/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.6680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.6185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.6215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.4772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.2787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.1737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.0273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.6346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.0571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.3579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.5675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.7079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.7970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.8466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.8682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.8463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.8129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 8.7694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.7173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.6592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 8.5968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.5307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.4613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 8.3898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.3171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 8.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.1704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.0965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.0231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.9505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.8070e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.7370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.6679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.5324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.9188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.8637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.8097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.6531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.6027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.4105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.1897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.1068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.0663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.0266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.7328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.6987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.6653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.6323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.5999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.5680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.5365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.5056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.4751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.3866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.3579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.3296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.3017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.2469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.2202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.0190e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.8816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.7958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.7750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.7544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.7341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.7140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.6942e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.6746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.6552e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.3473e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4147 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 823/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.0329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.9682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.9389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.9140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.8907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.8690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.8320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.8243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.8165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.8085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.7972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.7844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.7777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.7728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.7659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.7593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.7558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.7525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.7474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.7419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.7355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 9.7292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 9.7219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 9.7143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.7066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.7006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.6942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 9.6876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 9.6817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 9.6758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 9.6687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.6611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.6535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.6457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 9.6370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.6276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.6183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.6100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.6009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.5925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.5848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.5523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.5444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.5362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 9.5278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.5199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.5127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 9.5052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.4980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.4921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.4867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 9.4813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.4765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.4715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 9.4668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.4618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.4564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 9.4513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.4466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.4417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 9.4323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 9.4180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 9.4186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.4235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.4286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 9.4331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.4373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.4423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 9.4470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.4606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.4682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.4704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.4724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.4743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.4762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.4780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.4794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.4807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.4818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.4828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.4834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.4838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.4841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.4845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.4847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 9.5061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4162 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 824/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.3061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.9525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.1384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.0449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.9810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.9740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.9423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.8791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.7962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.7429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.7080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.6805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.6592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.6591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.6626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.6210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.6106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.5945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.5759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 8.5561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.5395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.5206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.5015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.4875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.4738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.4435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.4289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.4145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 8.3994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.3828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.3675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 8.3542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 8.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3107e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.1815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.1672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.1596e-08 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.1046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.1050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 8.1054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.1055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.1053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.1052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 8.1047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.1040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.1031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 8.1022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.1014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.1004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 8.0997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 8.0967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.0957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.0945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 8.0932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.0917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.0901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.0886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 8.0870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.8925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4188 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 825/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.5716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 9.0904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.0536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.7043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.4948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.3218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.1447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.1987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.1929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.1781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.1684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.1438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.1159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.0947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.0680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.0474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.9918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.9619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.9306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.9008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.8767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.8520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.8322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.8180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.7870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.7699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.7519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.7337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.7158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.6975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.6795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.6640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.6489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.6350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.6227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.6116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.6003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.5889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.5771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.5649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.5525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.5404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.5284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.5185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.5085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.4989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.4903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.4821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.4734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.4642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 7.4553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.4463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.4368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.4275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.4096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.4014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.3943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.3880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.3821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.3758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.3699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.3637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.3581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.3520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.3456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.3391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.3331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.3273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 7.3019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 7.3053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 7.3085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 7.3117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 7.3145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 7.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.3342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.3365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 7.3386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 7.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.3419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.3430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.3439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.3449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.3456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.3465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.3474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.3486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.3569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.3579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.3589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.3599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.3607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.3616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.3629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.3642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.3653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.3662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.3669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.3674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.3677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.3679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.3680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.3666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4174 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 826/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.8205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 36s 308ms/step - accuracy: 1.0000 - loss: 8.7042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 304ms/step - accuracy: 1.0000 - loss: 8.6937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 306ms/step - accuracy: 1.0000 - loss: 8.4315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 35s 304ms/step - accuracy: 1.0000 - loss: 8.2523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 304ms/step - accuracy: 1.0000 - loss: 8.1073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 303ms/step - accuracy: 1.0000 - loss: 8.0142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 7.8977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 7.7820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.6781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 7.5999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 7.5274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 7.4681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 7.4843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 7.5075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 7.5138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 7.5162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 7.5100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 7.5014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.4920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.4804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - 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7.4392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.4405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.4394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.4377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4203e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.2127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.2085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.2040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.1996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.1952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.1907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.1860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.1811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.1763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.1716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.1669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.1624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.1581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.1493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.1448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.1035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.5928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4205 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 827/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.4140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.8488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.8941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.7223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.5812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.5463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.5098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.3684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.9464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.9417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.9374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.9331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.9299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.9263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.9234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.9205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.8995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.8933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.8816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.8799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.8786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.8773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.8700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.8683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.7879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4227 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 828/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.2495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.1452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.3022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.1592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.0765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.8285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.7057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.5873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.8873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.8730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.8585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.8462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.8247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.8145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.8057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.7975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.7884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.7798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.7709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.7623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.7535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.7444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.7360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.6949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.6890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.6834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.6776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.6718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.6658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.6604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.6373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.6330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.6288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.6025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.5948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.5912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.5873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.5015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.1875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4247 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 829/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.7341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 6.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.0846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.9743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.8834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.7645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.6673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.5855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.5095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.4447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.4143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.3926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.3831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.3901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.3934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.3861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.3711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.3593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.3297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 5.3145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.3057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.2957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 5.2910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.2759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.2707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.2655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.2597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.2588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.2570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.2558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.2539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.2485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.2472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.2478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.2492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.2479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.2457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 5.2415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.2358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.2295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.2272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.2215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.2195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.2175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.2155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.2137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.2119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.2102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.2087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.2035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.2015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.1895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.1844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.1767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.1702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.1639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.1540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.1518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.1496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.1451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.1431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.1410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.8926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4263 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 830/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.5531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.7328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.8502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.7514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.6183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.5142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.4317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.3397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.2566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.1261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.0737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.0484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.0329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.0185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.9488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.9286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.9100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.8929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.8411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.8254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.8234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.8211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.8192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.8149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.8145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.8202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.8207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.8203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.8195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.8185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.8173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.8159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.8153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.8154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.8154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.8146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.8137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.8124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.8110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.8093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.8075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.8057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.8025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.8017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.8012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.8007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.8000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.7989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 4.7975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.7959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.7946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 4.7931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.7918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.7906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.7895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 4.7881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.7865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.7848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 4.7832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.7814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.7795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 4.7775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.7737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.7718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 4.7701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.7684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.7665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.7645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.7557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.7492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.7471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.7450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.7433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.7364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.7347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.7329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.7311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.7295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.7278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.7261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.7208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.7194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.7181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.7167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.7109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.5367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4280 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 831/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.2206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.2402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.2694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.1615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.0300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.9317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.8397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.7742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.6999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.6388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.5108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.4116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.3671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.3108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.2852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.2737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.2659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.2571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.2519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.2460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.2400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.2339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.2134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.1767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.1652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.1622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.1593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.1568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.1543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.1514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.1484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.1453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.1386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.1358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.1330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.1305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.1285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.1270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.1251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.1231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.1211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.1169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.1145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.1122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.1064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.1049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.1033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.1017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.1003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.0987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.0971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.0954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.0936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.0917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.0898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.0878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.0861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.0849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.0624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.0616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.9629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4295 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 832/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.5088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.7537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.7029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.3973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.2223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.1746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.1587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.1477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.1202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.1033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.0806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.0471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.0239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.0154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.0138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.0075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.9976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.9948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.9941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.9937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.9935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.9934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.9928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.9921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.9909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.9898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.9874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.9805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.9764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.9738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.9726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.9697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.9660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.9648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.9640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.9617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.9578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.9552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.9529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.9486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.9354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.9206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4310 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 833/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.4764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.7397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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3.8688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.8681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.8686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.8676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.8654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.8626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.8599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.8575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.8563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.8559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.8553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.8542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.8518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.8506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.8487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.8464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.8445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.8431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.8413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.8398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.8384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.8371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.8354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.8339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.8321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.8303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.8280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.8255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.7620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.7564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.7482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.7426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.7376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4326 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 834/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.9579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.0735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.9156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.8061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 3.7407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.7005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.6558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 3.6137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.5813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.5593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.5375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.5165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.5196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.5100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.5042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.4903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.4464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4341 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 835/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.1756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.8725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.5970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.2543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.9651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.7506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.5703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.2162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.0415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.9040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.8308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.8104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.7928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.7802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.7440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.6369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.6041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.5788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.5940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.6565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.6698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.6846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.6939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.6968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.6997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.7148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.7167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.7186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.7205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.8795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4350 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 836/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 3.7038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.0416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.4851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.5401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.5072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 5.4244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.3543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.2782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.2032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 5.1507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.0933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 5.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.8894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.8467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.8025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.7155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.6727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.6323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.5018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.4244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.1169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.6790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.9208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.1408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.3402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.5246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.6936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.8490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.1209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.2401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.3490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.6126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.7643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.9080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.0421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 8.1667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.2831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.3918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 8.4925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.5900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.6805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.7652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 8.8436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 8.9849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 9.0493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.1654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 9.2181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.2678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 9.3135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.3571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 9.3974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.4695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 9.5016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.5314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.5593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.5851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.6729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.6909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.7233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.7714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.7962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.8029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.8085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.8132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.8169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.8199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.8219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.8232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.8240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.8241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.8238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.8231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.8222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.8212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.8195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.8173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.8146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.8116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.8080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.8039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.7995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.7949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.7899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.7846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.7606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.7396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.7323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.7249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.7173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.7099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.8269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4296 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 837/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.6376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.9658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.0102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.8818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.8414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.8542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.8719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.8847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.9136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.9079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.8925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.7957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.7911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.7490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.7434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.7374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.7324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.7159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.7014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.6960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.6907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.6861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.6818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.6777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.6740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.6706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.6670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.6632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.6591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.6550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.6509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.6466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.6424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.6385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.6345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.6309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.6275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.6248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.6218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.6191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.6163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.6133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.5974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.5944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.5916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.5752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.5728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.5712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.5696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.5680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.5663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.5648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.5572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.5509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.5489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.5468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.5447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.5362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.5293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.5268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.5196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.2393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4345 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 838/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 5.0713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.9991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.0609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.8843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.6880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.4890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.2106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.1192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.0540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.0002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.6849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.6547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.6254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.5968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.5238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.5030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.4360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.4281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.4194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.4153e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.2973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.2832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.2467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.2420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.2376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.2333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.2292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.2252e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.1915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.1874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.1834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.1795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1677e-08 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.1295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.1150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.1075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.1038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.1004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.0933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.0834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.0754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.0643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.7496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4357 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 839/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.1323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.9825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.9763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.8267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.7125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.6003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.5141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.4415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 3.3806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.3124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.3026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.2640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.2404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.2267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.2264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.2257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.2191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.2154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.2117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.2087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.1891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.1796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.1677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.1633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.1140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.1127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.0291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4381 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 840/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.1082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.2074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.1623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.0621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.0229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.0054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.9829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.9593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.9497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.9343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.9315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.9266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.9194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.9116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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2.8712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.8647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.8609e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.9446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.9450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.9455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.9448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.9452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.9452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.9446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.9440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.9432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.9427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.9420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.9273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4400 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 841/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 4.3294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.2642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.0662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.9013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.4654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.2827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.2004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.0918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.0721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.0532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.0235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.0159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.0035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.9111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.9094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.9079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.9044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.9021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.9010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.8998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.8985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.8973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.8961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.8948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.6504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4417 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 842/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.7967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.8910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.9560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.8806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.7522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.6360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.5961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.5067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.5013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.4957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.4537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.4211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.4217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.4186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.4925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.5024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.5579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.5660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.5817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.5835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.5854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.5874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.5892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.5910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.5972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.5980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.5987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.5991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.5997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.6001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.6020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.6025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.6031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.6073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.6095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.6122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.6164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.6186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.6207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.6233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.6239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.6246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.6273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4434 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 843/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 3.3994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.2317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 3.1487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.9974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.6940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.6417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.5095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.4814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.4527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.4251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.4046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.3998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.3338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.3204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.3194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.3217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.3225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.2905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.2886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.2867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.2847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.2829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.2303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4449 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 844/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.2994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.3796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.1839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.1676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.1565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.1490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.1473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.1532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.1592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.1731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.1700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.1677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.1650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.1267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.1264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.1245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4462 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 845/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 3.3287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 3.1527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.9971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.8230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 2.7121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.6235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.5614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.3811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.3120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.3040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.2975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.2926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.2870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.2809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.2767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.2739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.2709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.2693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.2653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.2627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.2489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.2452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.2418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.2324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.2254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.2179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.2151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.2126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.2100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.2086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.2074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.2063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.2048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.2032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.2013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.1996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.1517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.1492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.1460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.1444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.1418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.1391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.1367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.1343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.1297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.1275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.0600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4481 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 846/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 1.7151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.9831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.0715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.0601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.0281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.9111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.9145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.9126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.9123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.9273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.9404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.9552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.9647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.9721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.9883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.9918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.9933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.9941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.9963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.9980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.9995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.0055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.0013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4483 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 847/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.5739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.3939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.2006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.0374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.7698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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1.7480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.7521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 1.7536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 1.7542e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 1.7704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 1.7744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.7770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.7794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.7820e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.7983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.7979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.7967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.7957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.7949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.7936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.7923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.7893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.7878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.7856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.7244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4505 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 848/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.4345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.7388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.8167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.8017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.7658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.7392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.7124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.6567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.6475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.6296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.6145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.6002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.5776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.5741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4518 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 849/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.2469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.1354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.7396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.6788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.6119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.6160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.6194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.6236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.6288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.6309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.6321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.6350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.6356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.6369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.6382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.6388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.6392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.6394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.6393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.6391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.6332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4536 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 850/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.8983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.8335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.8828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.8723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.8207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.8148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.8067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.8027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.7970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4543 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 851/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.3954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.3821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.4287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.4222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.4086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.3949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.4861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.4871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.4880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.4883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.4875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.4878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.4889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.4894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.4897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.4904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.4897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.4909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.4911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.4921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.4928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.4931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.4885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.4871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.4476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4559 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 852/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.8025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.7346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.6305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.5534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.5235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.4863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.4878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 1.4830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.4804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.4744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.4718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.4700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.4681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.4628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.4567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.4511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.4497e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.4296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.4265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.4243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.4226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.4209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4189e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.4166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.4156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.4148e-08 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.4122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.4126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.4134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.4141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.4147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4571 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 853/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.8388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.7181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.6820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4589 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 854/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.7110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.5244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.5049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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1.4388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4602 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 855/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.6125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.2446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.2430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4611 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 856/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.4721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.1151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.1032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.0952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.0877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.0886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.0907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.0945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.0969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.0997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.1034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.1054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 1.1070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 1.1090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 1.1100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 1.1105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.1114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.1110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.1109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.1106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 1.1101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 1.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 1.1106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.1109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 1.1111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 1.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.1220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4629 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 857/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.4033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.0914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.1206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 1.1090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.1422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.1403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.1362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4639 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 858/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.5932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.1007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.0182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.0204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.0257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.0332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.0405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.0426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4655 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 859/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.8615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.6838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 9.5345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.3805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.2656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.2100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.1650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.1782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.2046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.2379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.2522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.2642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.2678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.2781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.2931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.2979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.2995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.3106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.3172e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.3216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.3357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.3367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.3354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.3319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.3304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.3256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.3203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.3149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.3094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.3046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.3020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.3015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.3169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.3289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.3386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.3491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.3575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.3639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.3687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.3737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.3793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.3838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.3877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.3951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.4436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.4478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.4512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.4537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.4603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.4641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.4665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.4668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.4678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.4689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.4694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.4695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.4695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.4694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.4690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.4683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.4676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.4672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.4669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.4669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.4680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.4690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.4707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.4707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.4706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.4701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.4701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.4708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.4712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.4714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.4714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.4711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.4706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.4700e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.4680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 9.4648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4668 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 860/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.2130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.1590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.1216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.1195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.1060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.1009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.0668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.0649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.0632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.0581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.0510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.0441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0338e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.9987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.9906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.9825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.9746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.9669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.9594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.9522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.9451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.9389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.9327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.9263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.9199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.9135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.9069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.9004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.8939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.8875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.8816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.1832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4678 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 861/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.2861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.7022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.7208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.7369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - 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1.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.4863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.4615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.2981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.2961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.2943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.2924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.2905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.2886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.2868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.2850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.2782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.2765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.2748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.2731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.2664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4690 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 862/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.6117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.8388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.6789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3107e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.2647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.2143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.8880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.8714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.8530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.8350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.8153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.7969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.7770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.7565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.7362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.7177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.6995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.7154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.7309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.7470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.7589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.7706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.7795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.7875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.8044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8912e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.8964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.9012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.9235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.9277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.9316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.9345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.9374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.9406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.9432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.9454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.9487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.9516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.9562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.9606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.9649e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.9682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.9710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.9772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.9829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.9853e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.9890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.9923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.9958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.9989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.0019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.0043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.0067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.0124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.0158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.0191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.0206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.0188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.0179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.0188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.0200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.0236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.0241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.0247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.0250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.0249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.0247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.0247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.0244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.9887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4702 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 863/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.7871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.6910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.9679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.8546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.7437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.6572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.5714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.5405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.5473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.5589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.5524e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.5339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.5100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.4920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.4707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.4520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.4313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.4173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.4016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.3789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.3740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.3645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 9.3511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.3336e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.3146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 9.3018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.2868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.2706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.2583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 9.2510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.2436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.2376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 9.2324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.2246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.2159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 9.2063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.1972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.1866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.1744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 9.1626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.1269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.1068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.0851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.0510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.0139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.0022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.9697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.9376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.9038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.8623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.8359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.8095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.8005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.7914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.7824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.7737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.7650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.7564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.7490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.7417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.7340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.7263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.7182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.7103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.7023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.6942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.6865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.6795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.6725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.6540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.6306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.6250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.6195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.6139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.6083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.6026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.5973e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.5781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.5639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.5451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.9826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4715 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 864/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.6586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.8627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.8612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.6065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.4503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.3232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.2419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.1735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.1206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.1426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.1875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.2155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.2385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.2548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.2684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.2761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.2769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.2729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.2726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.2686e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.2683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.2724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.2771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.2818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.2913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.2909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.2917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.2935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.2963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.3024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.3084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.3223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3295e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3628e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3804e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.4034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.4185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.4281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.4365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.4390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.4414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.4433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.4448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.4481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.4511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.4538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.4561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.4582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.4602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.4634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.4668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.4705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4950e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.4992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.5047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.5144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.5195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.5236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.5278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.5313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.5364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.7088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4733 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 865/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.9715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.4697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.7921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.2005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.9870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.9556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.9254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.8961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.8670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.8385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.8100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.7816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.7535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.7258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.6225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.5993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.5771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.5552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.5345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.5138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.4935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.4732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.4531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.4331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.4135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.2980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.2822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.2665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.2509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.2355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.2204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.2054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.1907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.1764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.1622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.1479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.1340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.1200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.1060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.0921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.0782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 9.0645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.0510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.0376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 9.0245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 9.0121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.9757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.9413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.9085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.8979e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.6404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4741 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 866/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.3140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 8.3515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 8.3983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.1238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 7.8433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 7.6052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 7.4160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.2673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.1330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.0159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 6.9275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 6.8535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 6.7953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.8509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.8458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.8373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.8519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.8740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.8923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.9199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.9408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.9599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.9790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.9946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.0044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.0140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.0222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.0276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.0304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.0337e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.0564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.0505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.0506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.0499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.0484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.0470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.0472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.0523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.0540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.0553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.0560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.0564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.0568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.0565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.0575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.0587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.0596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.0605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.0630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.0571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.0565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.0563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.0563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.0557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.0552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.0506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.0501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.0505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.0509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.0516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.0525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.0545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.0545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.0548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.0550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.0560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.0577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.0668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.0683e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.0697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.0709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.0720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.0736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.0752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.2726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4748 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 867/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.3872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 8.1511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 8.4775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 8.2309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 8.0120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 7.8800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.8053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.6937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.5720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.4813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.4465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.4578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.4703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.4852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.5078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.5154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.5191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.5129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.5051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.4755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.4634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.4524e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.4438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.4368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4241e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.4246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.4295e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.4350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.4522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.4554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.4566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.4572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.4539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.4532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.4527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.4517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.4508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.4509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.4502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.4485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.4458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.4431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.4394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.4349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.4302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.4255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.4205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.4156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.4117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.4084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.4046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.4005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.3959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.3915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.3878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.3841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.3803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.3765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.3727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.3612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 7.3584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 7.3551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 7.3527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 7.3505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 7.3479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 7.3451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 7.3364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.3348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.3328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 7.3308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.3285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.3261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.3237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 7.3210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.3185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.3163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 7.3142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.3122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.3101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 7.3085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 7.3004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.2980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.2953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 7.2929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.2904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.2879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 7.2857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 7.2785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.2735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.2655e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 7.0298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4756 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 868/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.3786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.2686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.1989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.1405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 1.0875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.0423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.7257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.4467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.2206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.0230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.8515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.7197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.6105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.5529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.4922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.4284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.3622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.3126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.2648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 8.2156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - 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7.9598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.8878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.7858e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.7312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.7398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.7470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.7616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.7666e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.7057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.7003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.6671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.6590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.6619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.6651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.6682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.6708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.6740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.6771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.6798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.6829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.6856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.6881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.6937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.7062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.7125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.7186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.7293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.7425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.7559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 8.1139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4769 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 869/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.2300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.7883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.3797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.0889e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.0423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.9513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.8588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.8133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.7779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.7734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.7695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.7698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.7624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.7489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.7335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.7197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.7000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.6246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.6153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.6102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.6045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.5670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.5508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.5335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.5163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.4704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.4578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.4468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.4393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.4300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.4106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.4002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.3793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.3657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.3512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.2982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.2937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.2890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.2839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.2794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.2751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.2590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.2562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.2535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.2502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.2465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.2425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.2385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.2195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.2060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1823e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.1572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.1526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.1482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.1436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1340e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.1239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.1078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.1026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.0592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0545e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0453e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.0407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.4957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4775 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 870/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.7297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.1407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.1006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.9145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.8161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.6440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.5703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.4995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.4385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.3774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.3231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.2696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.2156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.1650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.1189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.0731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.0310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.9911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.9568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.9230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.8908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.8585e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.8260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.7933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.7609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 8.7294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.7025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.6757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.6752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.6750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.6741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.6714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.6679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.6637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.6593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.6539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.6474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.6410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.6341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.6267e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.6188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.6114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.6051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.5976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.5899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.5814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.5726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.5630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.5526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.5419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.5316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.5210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 8.5105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.5003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.4906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.4803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.4697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.4593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.4487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.4378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.4266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.4153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.4040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.3926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.3820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.3718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.3616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.3511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.3406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.3300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.3195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.3089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.2982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.2875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.2770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.2663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.2557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.2455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.2353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.2251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.2149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.2048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.1947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.1846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.1743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.1642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.1544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.1446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.1350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.1255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.1162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.1070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.0977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.0884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.0792e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.0698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.0605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.0511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.0419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.0328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 6.9400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4791 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 871/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.3132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 8.6582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 8.1104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.6574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.4149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.2282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 7.1035e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.9852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.8681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.7636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 6.6960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.7012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.7170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.7357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 6.7506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.7480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.7372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.7181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 6.6945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 6.6695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.6547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.6419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.6310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.6200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.6127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.6083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.6064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.6007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.5936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.5847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.5756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.5647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.5525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.5424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.5327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.5220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.5121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.5035e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.4952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.4663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.4263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.3692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.3458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.3212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.3068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.2866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.2829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.2790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.2747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.2478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.2447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.2416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.2274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2202e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.2174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2150e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2034e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.2009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.1984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.1964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.1942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.1885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.1808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.9659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4806 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 872/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.5840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.8883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.3742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.9959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.6743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.4652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.2783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.1007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.9593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.8436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.0254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.1527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.3636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.4277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.4701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.4972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.5117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.5171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.5133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.5111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.5055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.4962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.4862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.4746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.4633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.4477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.4312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.4121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.3918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.3703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.3467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.3240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.3033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.2815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.2617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.2456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.2313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.2153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.1987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.1829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.1666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.1495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.1322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.1151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.0995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.0862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.0735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.0624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.0532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.0429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.0324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.0227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.0124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.0016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.9900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.9783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.9668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.9163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.9082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.9006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.8934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.8860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.8781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.8703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.8627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.8550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.8478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.8409e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.8342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.8270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.8201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.8130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.8057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.7983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.7931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.7877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.7823e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.7770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.7716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.7662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.7609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.7553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.7498e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.7281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.7230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.7177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.7124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.7023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.6977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.6728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.6680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.6634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.6592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.6548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.6507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.1723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4818 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 873/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.8528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 7.3042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.8801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.5445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.2737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.0841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.9369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.8139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.7056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.6150e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.5592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.5360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.5075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.5029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.4812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.4685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.4605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.4616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.4657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 5.4679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.4669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.4651e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.4540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.4529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.4518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.4504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.4484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.4460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.4450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.4435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.4422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.4416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.4410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.4394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.4381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.4364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.4343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.4352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.4357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.4361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.4377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.4389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.4400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 5.4418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.4434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.4464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.4460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 5.4452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.4441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.4436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.4431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.4429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.4454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.4482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 5.4511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.4539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.4563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 5.4587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.4609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.4627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.4644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 5.4663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.4677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.4691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.4707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.4752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.4794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.4821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.4858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.4908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.4917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.4924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.4932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.4941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.4949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.4956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.4965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.4973e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.4980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.4988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.4993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.5011e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.5015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.5019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.5023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.5516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4830 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 874/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.6335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.5181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.5378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 8.2189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.8935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.7387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.5697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.4068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.0605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.9722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.8232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.7515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.5518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.4881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.4316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.3758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.3385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.3000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.2673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.2385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.1858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.1605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.1355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.1128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.0183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.9971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.9463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.7891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.7407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.7321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.7246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.8095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.8893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.1717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.3493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.4538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6336e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.7116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.7480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.7830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.8177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.8503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.8813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.0139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.0379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.0612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.0848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.1075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.1689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.1873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.2368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.3051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.3177e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.3296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.3410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.3517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.3622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.3724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.3820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.3911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.3998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.4160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.4446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.4632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.4843e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.0621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4810 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 875/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.0254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.6426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.1520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.7564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.4251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.1478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.9238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 7.7782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.6530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.5419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.4516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.3716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.3018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.2310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.1597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.0971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.0358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.9732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.9151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - 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7.4638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.5259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.5781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.6276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.6717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.7086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.7421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.7694e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.6662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.6383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6295e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.6021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.5123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.5059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.4688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.4625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.4561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.6639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4844 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 876/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.6323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.8937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.6042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.1406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.7452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.3890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.0977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.8485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.7064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.5211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.5740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.6068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.6200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.6114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.5963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.5778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.5617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.5483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.5350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.5238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.5139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.5022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.4914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.4930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.5137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.5296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.5425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.5531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.5625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.5689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.5734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.5797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.5842e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.5837e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.5808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.5773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.5748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.5718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.5677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.5640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.5611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.5583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.5554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.5518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.5472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.5420e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.5362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.5305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.5246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.5187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.5138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.5092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.5045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.4998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.4963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.4924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.4881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.4832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.4783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.4729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.4674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.4619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.4562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.4505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.4454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.4408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.4359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.4313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.4266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.4218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.4167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.4112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.4056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.3999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.3940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.3887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.3836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.3784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.3730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 6.3675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 6.3459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.3404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.3352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 6.3299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.3247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.3196e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 6.3148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.3097e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.3045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 6.2952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 6.2828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 6.2704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 6.2542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.2500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.2458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 6.3741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.4990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.6207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.7399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 6.8583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.0946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.4624 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 877/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.5260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.6558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.1839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.5810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.6693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2108e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8120e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.3986e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.8380e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8496e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0416e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2887e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4993e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.7014e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9037e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.1015e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3881e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.6539e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9236e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1860e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.4434e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 0.9999 - loss: 3.6984e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 0.9999 - loss: 3.9571e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9999 - loss: 4.2139e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9999 - loss: 4.4552e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 0.9999 - loss: 4.6862e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 4.9146e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 5.1403e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 5.3648e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 0.9999 - loss: 5.5776e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9999 - loss: 5.7897e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9999 - loss: 5.9982e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 0.9999 - loss: 6.1979e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9999 - loss: 6.3878e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9999 - loss: 6.5733e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 0.9999 - loss: 6.7518e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9999 - loss: 6.9207e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 7.0801e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 7.2306e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 0.9998 - loss: 7.3722e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9998 - loss: 7.5044e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9998 - loss: 7.6272e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 0.9998 - loss: 7.7410e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9998 - loss: 7.8485e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9998 - loss: 7.9500e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 0.9998 - loss: 8.0454e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 8.1345e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 8.2176e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 8.2939e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 0.9998 - loss: 8.3665e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 8.4346e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 8.4977e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 0.9998 - loss: 8.5560e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 8.6097e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 8.6589e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 0.9998 - loss: 8.7037e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 8.7444e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 8.7826e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 8.8173e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 0.9998 - loss: 8.8489e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 8.8772e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 8.9041e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 0.9998 - loss: 8.9299e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 8.9526e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 8.9726e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 0.9998 - loss: 8.9901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 9.0059e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 9.0201e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 9.0329e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 0.9998 - loss: 9.0454e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 9.0562e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 9.0658e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 0.9998 - loss: 9.0735e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 9.0799e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 9.0851e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 0.9998 - loss: 9.0898e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 9.0931e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 9.0953e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 9.0964e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 0.9998 - loss: 9.0963e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9998 - loss: 9.0952e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9998 - loss: 9.0934e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 0.9998 - loss: 9.0905e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9998 - loss: 9.0868e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9998 - loss: 9.0822e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 0.9998 - loss: 9.0769e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 9.0709e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 9.0641e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 9.0565e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 0.9998 - loss: 9.0481e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 9.0392e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 9.0296e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 0.9998 - loss: 9.0195e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 9.0088e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 8.9976e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 0.9998 - loss: 8.9859e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 8.9737e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 8.9610e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 8.9479e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 0.9998 - loss: 8.9343e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 8.9203e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 8.9059e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 0.9998 - loss: 8.8912e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 8.8761e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 8.8607e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 0.9998 - loss: 8.8450e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 8.8290e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 8.8128e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 8.7962e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9998 - loss: 8.7795e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 8.7625e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 8.7453e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9998 - loss: 8.7279e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 8.7103e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 8.6926e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 8.6746e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9998 - loss: 8.6566e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 6.5063e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9769 - val_loss: 0.3711 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 878/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 7.8860e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.4138e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.2041e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.9136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 6.7358e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.5805e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 6.4543e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.3229e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 6.2029e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.1116e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 6.0448e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.9776e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.9222e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.8712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 5.8271e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.7781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.7305e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.6843e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.6407e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.5963e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.5516e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.5099e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4721e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 5.4032e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.3704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.3391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.3070e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 5.2761e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.2454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.2149e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.1845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.1540e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.1240e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.0954e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.0670e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.0389e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.0113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9310e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9050e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8798e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8546e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8053e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7818e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7586e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.7145e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.6930e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.6717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.6507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.6299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.6092e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.5888e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.5686e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.5488e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.5295e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.5104e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4916e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4732e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4371e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.4194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.4019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3846e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3675e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.3505e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3338e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3175e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.3014e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.2854e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.2698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.2544e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.2391e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.2239e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.2090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.1943e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.1796e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.1651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.1508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.1367e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.1228e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.1090e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0955e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0426e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0297e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0169e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0042e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9917e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.9672e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9551e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.9313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9196e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.9079e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8964e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8849e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8623e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8512e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8402e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8293e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8184e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8078e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7866e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7762e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7659e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7556e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7454e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7353e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7253e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7154e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7055e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.5369e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3740 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 879/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5298e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5015e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5054e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4428e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4238e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4101e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3948e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3806e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3723e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3715e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3698e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3676e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3649e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3619e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3590e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3571e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3548e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3526e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3507e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3495e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3476e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3458e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3439e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3419e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3396e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3373e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3349e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3331e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3296e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3280e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3248e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3230e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3213e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3195e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3176e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3158e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3142e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3128e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3113e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3084e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3071e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3058e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3045e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3031e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3016e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3001e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2987e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2972e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.2958e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2945e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2918e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.2905e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2892e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2878e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2865e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2851e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2836e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2822e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2807e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2793e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2779e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2766e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2753e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2740e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2727e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2714e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2701e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2688e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2674e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2661e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2648e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2635e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2622e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2609e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2597e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2584e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2572e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2559e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2547e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2534e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2521e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2509e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2484e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2472e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2459e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2447e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2423e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2411e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2386e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2374e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2361e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2348e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2337e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2313e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2301e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2289e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2278e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2266e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2255e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2243e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2232e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2220e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2209e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2197e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2186e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0845e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3766 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 880/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.3011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 8.7528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.9387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.9016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.8752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.8385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.7779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.7148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.6431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.5987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.5807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.5605e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.5514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.5455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.5459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.5339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.5225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.5107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 8.4987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.4814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.4644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.4489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.4435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.4202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.4124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.4052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.3885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.3814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.3799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3786e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.3788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.3736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.3707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.3674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.3639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.3603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.3585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.3556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 8.3524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.3492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.3462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 8.3422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.3382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.3338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.3296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 8.3245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.3145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.3055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.3009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 8.2966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.2788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 8.2626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 8.2404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.2352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.2301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 8.2260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.2219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.2176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 8.2135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.2093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.2049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.2004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 8.1963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.1921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.1877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.1835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.1793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.1748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.1702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.1645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.1659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.1674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.1685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.1693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.1700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.1706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.1704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.1699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.1692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.1684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.1676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.1667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.1658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.1649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.1640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.1629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.1617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.1604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.1589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.1573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.1557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.1508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.9503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3785 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 881/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 6.6949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 6.8408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.8898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.7575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.6630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.5780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.4456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.3546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.3510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.3292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2952e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.2881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.2855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.2870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.2909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.2904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.2888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.2871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.2850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.2834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.2813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.2766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.2745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.2675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.2608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2545e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.2334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.2286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.2201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.1974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.1943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.1574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.1543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.1511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1446e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.1173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.1037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.0932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.0896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.0860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.0826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.0791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.6732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3833 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 882/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.6850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.8607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.9914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.9402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.9202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.8965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.8718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.8065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.7792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.7691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.7589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.7505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.7489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.7507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.7420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.7366e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.7337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.7279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.7209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - 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4.7255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.7232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.7209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.7189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.7183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.7171e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.6823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.6812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.6800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.6744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.6730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.6705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.6669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.6657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.6644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.6630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.6616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.6572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.6530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.6475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.6433e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.6374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.4609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9771 - val_loss: 0.3876 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 883/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.3422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.2278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.1009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.0937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.0989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.1007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.1000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.0968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.0926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.0885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.0824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.0784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.0752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.0741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.0709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.0670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.0630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.0586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.0542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.0491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.0438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.0398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.0354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.0319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.0287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.0258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.0222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.0186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.0150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.0115e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.0079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.0040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.0002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.9972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.9946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.9920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.9897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.9877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.9852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.9828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.9803e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.9778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.9749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.9720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.9693e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.9670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.9646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.9623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.9603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.9585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.9562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.9540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.9518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.9495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.9472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.9449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.9406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.9387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.9367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.9349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.9333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.9314e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.9294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.9254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.9211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.9189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.9169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.9150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.9131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.9113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.9096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.9078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.9060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.9042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.9023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.9004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.8985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.8965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.8947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.8929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.8911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.8894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.8878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.8860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.8842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.8825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.8807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.8788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.8769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.8751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.8733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.8715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.8697e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.8634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.8601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.6597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3910 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 884/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.0637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.1738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.3644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.3632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.3090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.2335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.2132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.2005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.2866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.3280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.3447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.3597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.3741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.3858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.3956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.4038e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.4109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.4166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.4209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.4249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.4291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.4327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.4364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.4407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.4447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.4537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.4542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.4589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.4584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.4578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.4570e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.4562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.4555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.4540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.4535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.4529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.4521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.4511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.4499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.4487e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.4472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.4457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.4441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.4427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.4414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.4401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.4388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.4376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.4363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.4349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.4336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.4322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.4269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.4256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.4244e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.4232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.4220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.4194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.4167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.4152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.4159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.4167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.4175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 3.4181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.4189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.4196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 3.4203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.4208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.4212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.4215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 3.4217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.4219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.4220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 3.4220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.4221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.4220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 3.4219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4218e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 3.4213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.4210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.4207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 3.4203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 3.4182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.3556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3930 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 885/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 3.2373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.2933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.1703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.0040e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.9866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.9889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9428e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9380e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9320e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9137e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8575e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8374e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.8361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.8304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8275e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.8261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.8221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.8167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.8126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.8089e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.8045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.7964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.6627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3961 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 886/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.9810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.9790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.7225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.6046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5864e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.5727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.5495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.4968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4847e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.4806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.4712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.4642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.4596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.4494e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.4458e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.4452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.4368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.4353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.4340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.4171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.4149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.4129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4114e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.4099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.4074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4058e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.4051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.4026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3983e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3989 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 887/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.5011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.4899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.3135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.2050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.1886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.1957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1924e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.1866e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.1774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1757e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.1622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.1596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1586e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1567e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.1558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.1513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.1486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.1470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1463e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.1459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.1429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.1408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.1397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1273e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.1192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.1151e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.1127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.0409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4013 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 888/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.8263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.9175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.9613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.9395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.9222e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.9164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.9100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.8947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.8956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.8955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.8949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.8965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.8860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.8828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.8802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.8770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.8776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.8758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.8741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.8744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.8754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.8751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.8745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.8740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.8735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.8724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8720e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.8714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.8707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.8705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.8703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.8700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8692e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.8686e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.8676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.8667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.8651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.8640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8637e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.8630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.8612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8607e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.8599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.8587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.8572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.8559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8555e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.8542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.8075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4035 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 889/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.2031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.0976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0182e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.8288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.8020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.7929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7770e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7657e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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1.7415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.7373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7356e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.7322e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6996e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4056 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 890/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.3462e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.5048e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.4885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4883e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.4796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4933e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4943e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4936e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.4927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.4923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.4898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4954e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4977e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4987e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4997e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.5000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.5001e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.5003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.5004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5006e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.5010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.5012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.5011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.5010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.5009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.4915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4077 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 891/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.5783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.5476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.5154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.4905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.4702e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.4500e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.4166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.4047e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.3917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.3852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3836e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.3845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.3839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.3832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.3831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.3825e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.3813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.3811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.3811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.3816e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3835e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3840e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.3856e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.3865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3872e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.3874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.3888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.3900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.3908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3909e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.3913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.3913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3910e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.3908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.3902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.3899e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.3893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3886e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3884e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4084 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 892/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3055e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3150e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.3284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3300e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3313e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3322e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.3283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.3276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.3267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.3264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.3256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3245e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3226e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.3217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.3204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3201e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.3196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3163e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3156e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.3100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.3088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.3071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4101 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 893/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 307ms/step - accuracy: 1.0000 - loss: 1.3904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.3175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3531e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3104e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2961e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2920e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2870e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.2842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2821e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.2800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.2707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.2556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2521e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.2512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.2501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2441e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2437e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2435e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2404e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2400e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2387e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2371e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2367e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2363e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2353e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2328e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2301e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2283e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2280e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2276e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2243e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2232e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2216e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2196e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4123 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 894/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.0290e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0486e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 1.0408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0519e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.0596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.0690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0723e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0811e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0812e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0802e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0793e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0783e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0779e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0777e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0776e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0774e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0750e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0738e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0732e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.0535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4141 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 895/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.9687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.6918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0119e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0202e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0082e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.9455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.9297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.9276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.9293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.9182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.9043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.8507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.8300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.8159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.8064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.7941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.7930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.7920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.7890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.7851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.7801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.7752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.7710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.7702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.7706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.7719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.7744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.7780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.7791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.7791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.7781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.7763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.7737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.7695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.7644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.7606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.7565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.7526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.7426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.7402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.7410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.7490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.7501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.7511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.7516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.7517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.7513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.7509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.7508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.7504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.7501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.7502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.7503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.7498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.7490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.7477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.7472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 9.7419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.7393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 9.7382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.7373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.7362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 9.7349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.7302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.7291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.7284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.7277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.7268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.7262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.7258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.7251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.7244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.7239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.7233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.7224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.7252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.7279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.7308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.7334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.7360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.7467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.7529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.7596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.9531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4163 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 896/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.1774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 303ms/step - accuracy: 1.0000 - loss: 9.0299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.4680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.4853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.4468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.3996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.3302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.1930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.1295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.1013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.0742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.0622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.0817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.9929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.9898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 8.9873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.9863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 8.9866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.9875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.9667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.9648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 8.9633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.9633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.9642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.9641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 8.9641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.9635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.9627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 8.9614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.9598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.9589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 8.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.9596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.9643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.9691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 8.9750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.9803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.9852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 8.9901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.9945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 8.9983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.0020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.0055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.0091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.0124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.0155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.0229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.0258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.0284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.0306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.0328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.0345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.0358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.0371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.0386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.0400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.0416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.0430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.0461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.0486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.0510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.0530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.0549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.0577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.0588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.0602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.0614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.0625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.0637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.0649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.0659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.0666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.0671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.0676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.0680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.0683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.0685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.0688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.0689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.0689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.0684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.0678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.0673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.0666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.0653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.0648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.0641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.9834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4178 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 897/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.0459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0339e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.0130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.9402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.7660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.6155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.3324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.2760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.2313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.1965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.1539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.1105e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.0429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.9348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.9065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.8948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.8869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.8794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.8697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.8574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.8436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.8304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.8028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.7926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.7816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.7709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.7620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.7547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.7463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.7380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.7297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.7207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.7020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.6929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.6699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.6503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.6341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.6278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.6219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.6170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.6019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.5977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.5936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.5854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.5818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.5780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.5739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.5625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.5549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.5517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.5486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.5454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.5421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.5391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.5362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.5337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.5309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.5280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.5253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.5228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.5200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.5169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.5043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.5011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.4954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.4876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.4773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.4692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.4615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.4512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.4433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.1810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4196 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 898/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.8648e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.6653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.5714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.2729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.0183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.9542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.9322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.8923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.8774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.8791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.8882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9090e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.9205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.9132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.9028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.9006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.8943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.8819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.8725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.8709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.8683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.8665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.8679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.8707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.8697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.8666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.8631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.8581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.8526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.8608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.8691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.8776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.8852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.8932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.9011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.9209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.9375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.9524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.9653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.9758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.0004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.0079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.0142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.0206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.0268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.0333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.0387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.0432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.0468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.0500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.0525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.0538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.0549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.0564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.0576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.0585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.0597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.0611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.0620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.0626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.0630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.0628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.0714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.0795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.0950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.1086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.1691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.1730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.1768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.1809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.1844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.1874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.1971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.1991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.2033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 8.2098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 8.2157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.2166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.2172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 8.2178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.2186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.2191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 8.2206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.2223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.2242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.2258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 8.2273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.2286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.2297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 8.2307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.2317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.2327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.2337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 8.2345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 8.3339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4209 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 899/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.1851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.3772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.3284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.2251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.9441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.8289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.7678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 7.7148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 7.6613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.6272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.5890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.5829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.5711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.5633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.5524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.5394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.5237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.5096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.5009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.4917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.4885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.4780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.4736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.4687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.4650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.4614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.4555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.4529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.4490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.4446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.4400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.4361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.4329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.4286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.4252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.4224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.4158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.4129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.4115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.4091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.4063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.4033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.3961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.3844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.3720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 7.3637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3569e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.3531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.3416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.3349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.3278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.3189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.3136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.3123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.3108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.3091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.3073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.3055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.3034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.3011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.2968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.2952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.2942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.2923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.2907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.2889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.2884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.2878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.2871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.2862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.2854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.2848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.2841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.1995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4226 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 900/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.0354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.3796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.5381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.4744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.3924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.3422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.2990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.2545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.1953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.1570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.1423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.1256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.1168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.1137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.1076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.0714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.0601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 7.0491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.0435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.0397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.0385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 7.0374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.0364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.0390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 7.0393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.0386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.0370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.0347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.0291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.0288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.0286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.0283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.0231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.0177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.0153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.0122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.0089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.0053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.9993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.9960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.9930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.9906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.9887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.9862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.9836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.9809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.9780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.9751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.9720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.9602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.9535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.9464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.9373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.9353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.9336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.9318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.9283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.9263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.9173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.9106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.9084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.9063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.9045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.9025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.9004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.8984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.8962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.8939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.8916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.8893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.8873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.8859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.8849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.8842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.8837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.8831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.8824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.8799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 6.8752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 6.7266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4243 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 901/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.5206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 7.8658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.4944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 7.1419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.8632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.6628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.5243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 6.4150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.3340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.2753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 6.2395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.2148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 6.2031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.2029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 6.2222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.2305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.2333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 6.2304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.2254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.2203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.2151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 6.2104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.2142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.2157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 6.2169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.2185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.2251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 6.2288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.2311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.2320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.2324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 6.2312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.2315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.2317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 6.2321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.2352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.2372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.2398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 6.2444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.2482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.2513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 6.2536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.2549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.2549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.2545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 6.2539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.2538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.2531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 6.2523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.2520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.2511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.2498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.2483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.2465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.2444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.2421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.2404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.2396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.2382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.2369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.2357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.2332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.2302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2299e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.2151e-08 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.2027e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.2008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.1988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.1965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.1952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.1917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.1874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.1838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.1792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.0501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4257 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 902/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.7011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 6.3622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.6375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.5626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 6.4452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.3295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 6.2658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 6.2078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 6.1500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 6.0998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0254e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.0293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.0187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.0015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.9727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.9601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.9476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.9284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.8931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.8866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.8843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.8821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.8797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.8703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.8646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.8593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.8513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.8456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.8402e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.8136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.8118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.6115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4273 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 903/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 6.8767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.5505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.3712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.1512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.9975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.9249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.8643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.9550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.9976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.0476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.0734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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5.9203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.9095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.8983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.8864e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.8747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.8631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.8532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.8439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.8348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.8266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.8203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.8144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.8079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.8015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.7946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.7871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.7796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.7720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.7654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.7585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.7524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.7465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.7415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.7361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.7302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.7243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.7183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.7124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.7064e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.6414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.6384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.6355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.6326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.6296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.6266e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.6028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.6009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.5988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.5966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.5946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.5925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.5904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.5881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.5861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.5845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.5828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.5797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.5767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.5750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.5735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.5720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.5706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.5690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.5674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.5658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.5643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.5628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.5613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.5604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.5592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.5580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.5547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5522e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.5511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.5500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.4097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4287 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 904/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.3331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1236e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.7142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.3541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 9.0389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.8354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.6592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.5020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 8.3588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.2337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.1215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.0124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.9078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.8102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 7.7171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.6296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.5449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.4669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 7.3953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.3281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.2663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 7.2102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.1582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.1072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 7.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.0109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.9663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 6.9237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.8820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.8419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.8059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.7720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.7398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.7099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 6.6821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.6677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.6532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.6384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.6250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.6113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.5975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 6.5839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.5716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.5594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 6.5472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.5353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.5241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 6.5125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.5011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.4896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.4782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.4672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.4561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.4452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 6.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.4150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 6.4056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.3965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.3870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 6.3774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.3678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 6.3380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.3280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.3183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 6.3088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 6.2730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 6.2471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 6.2134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.2053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.1973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.1894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.1671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.1379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.1170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.1103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.1038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.0978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.0736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.0676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.0616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.0558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.0503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.0448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.0394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.0341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.0289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.0237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.0186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.0134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.0082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.0031e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.9830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.3920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4302 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 905/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 5.0717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.2817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.4246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.3452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.2987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.2019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.1557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.1071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.0689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.0440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 5.0153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.9771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.9221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.9108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.9016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.8482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.8432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.8380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.8269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.8130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.8112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.8071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.8082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.8087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.8094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.8106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.8119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.8131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.8164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.8169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.8261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.8351e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.8432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.8513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.8596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.8680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.8756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.8827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.8892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.8951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.9006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.9055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.9101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.9149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.9192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.9233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.9274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.9355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.9464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.9589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.9669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.9743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.9818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.0003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.0012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.0026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.0081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.0091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.0100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.0110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.0147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.1194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4313 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 906/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.1654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.4097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.5422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.5852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.5872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.5696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.5476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.5214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.4957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.4853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.4764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.4720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 4.4738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.4795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.4767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.4739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.4694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.4502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.4431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 4.4315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.4207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.4171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.4137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.4099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.4083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.4087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.4082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.4079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.4078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.4089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.4096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.4108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.4118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.4125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.4130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.4128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.4129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.4177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.4205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.4210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.4212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.4215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.4220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.4225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.4225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.4218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.4221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.4222e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4268e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.4287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.4482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4328 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 907/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.2088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 5.3051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 5.3549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 5.2001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.0596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.9507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 4.8559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 4.7692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 4.6890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.6234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 4.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 4.5208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.5124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.5015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.4893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.4760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.4625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.4480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.4345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.3943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.3873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 4.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 4.3689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.3601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.3509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.3423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.3337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.3264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 4.3205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 4.3048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.3011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 4.2880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 4.2772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 4.2664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 4.2594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.2570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.2545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 4.2525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 4.2473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.2474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.2476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 4.2478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.2489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 4.2497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.2506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.2512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.2514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.2526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.2518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.2512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.2507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.2493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.2487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.2481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.2476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.2471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.2469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.2468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.2467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.2459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.2459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.2458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.2456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.2455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.2455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.2456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.2455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.2436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.2038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4340 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 908/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.3817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.4513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.2148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.4250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.5086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.5614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.5354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.4391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.4230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.4121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.4030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.3963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.3856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.3734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.3605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.3481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.3342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.3207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.3074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2976e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.2719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.2557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.2506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.2455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.2395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.2342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.2662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.2844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.2919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.2988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.3046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.3094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.3134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.3626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.3644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.3660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.3676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.3687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.3696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.3701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.3707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.3704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.3703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.3704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.3703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.3704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.3705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.3706e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.3681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.3673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.3666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.3660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.3654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.3646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.3638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.3646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.3653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.3661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.3666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.3671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.3676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.3682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.3688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.3694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.3701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.3710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.3712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.3714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.3715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.3715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.3715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.3716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.3716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.3716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.3716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.3717e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.3716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.3715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.3712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.3709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 4.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 4.3687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 4.3095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4355 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 909/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 3.7195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.0218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.9907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.9503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.9288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.9036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.8543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.8373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.8794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.9276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.9458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.9470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.9475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.9479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.9484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.9484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.9479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.9471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.9468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.9441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.9407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.9369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.9240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.9169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.9151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.9083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.9028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.8998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.8937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.8803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.8731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.8676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.8647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.8619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.8573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.8540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.8528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.8514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.8501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.8412e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.8385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.8343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.8314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.8258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.8229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.8204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.8128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4368 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 910/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.2681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.5802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.7925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7100e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.6326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.5765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.5458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.5547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.5557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.5578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.5579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.5541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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3.5765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.5783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.5796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.5800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.5804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.5804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.5808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.5810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.5813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.5833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.5853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5886e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.5912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.5939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.5955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.5983e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.5999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.6002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.6069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.6071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.6062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.6055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.6049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.6038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.6026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.6016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.6008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.6000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4382 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 911/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.4926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.3526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.3817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.4003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 3.4169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.4363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.4534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.4620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.4699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.4603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.4564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.4607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.4660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.4657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.4625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.4612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.4599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.4556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.4568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.4568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.4567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.4557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.4550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.4536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.4514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.4492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.4479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4474e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.4459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.4400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.4398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.4389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.4378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.4373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.3537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4393 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 912/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.9692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.5859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 4.3699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.1496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.9915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.8897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.8049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.7255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.6716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.6289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.5981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.5708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.5551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.5425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.5380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4230e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.4012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 3.3818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.3447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.3353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.3261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.3178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.3096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 3.3021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2062e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.1982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.1946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.1915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.1892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.1866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.1857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.1852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.1838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.1836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.1833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.1804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.1451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4407 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 913/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.6373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.9750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.9737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.9471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.9227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.8958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.8712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.8554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.8527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.8524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.9076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.9318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.9366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.9381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.9393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.9406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.9415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.9430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.9449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.9465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.9484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.9509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.9524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.9536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.9543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.9546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.9546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.9545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.9543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.9547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.9547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.9548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.9563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.9581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.9594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.9606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.9613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.9619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.9842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.9892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 2.9986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.0037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 296ms/step - accuracy: 1.0000 - loss: 3.0125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.0165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.0202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.0237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 296ms/step - accuracy: 1.0000 - loss: 3.0270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.0300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.0330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 296ms/step - accuracy: 1.0000 - loss: 3.0357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.0383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.0409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.0435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 296ms/step - accuracy: 1.0000 - loss: 3.0458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.0480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.0500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 296ms/step - accuracy: 1.0000 - loss: 3.0520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.0702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.0715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.0721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.0726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.0731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.0735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.0738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.0741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 296ms/step - accuracy: 1.0000 - loss: 3.0743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.0746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.0748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 296ms/step - accuracy: 1.0000 - loss: 3.0751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.0753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.0756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.0758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 296ms/step - accuracy: 1.0000 - loss: 3.0760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.0761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.0763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 296ms/step - accuracy: 1.0000 - loss: 3.0764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.0765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.0768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.0772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 1.0000 - loss: 3.0775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 3.1099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4423 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 914/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.6762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.8587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.9438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.8020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.7802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.7600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.7504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.7564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.7709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.7917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.8095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.8215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.8287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.8511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.8552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.8591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.8626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.8670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8703e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8724e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.9012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.9045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.9064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.9078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.9085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.9084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.9081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.9070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.9054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.9039e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.9020e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9009e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.9004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.8332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4436 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 915/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.0553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.2036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.3618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.8372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.8241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8238e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.8236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.8191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.8109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8065e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.8044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8023e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.8006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.7992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7951e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.7931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7908e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.7829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.7805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.7818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.7818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.7817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.7814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.7808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.7789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.7776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.7777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.7776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.7773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.7769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.7763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.7757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.7750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.7722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.7710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7697e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.7690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.7656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.7641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.7625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.7598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.7578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.7563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7557e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.7540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.7520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.7505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.7490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.7476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.7459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.6924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4448 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 916/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.4944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.5146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.5073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.4909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 2.4854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4790e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 2.4860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.4893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.4944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.5038e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.5116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.5160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.5191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.5206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.5139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.5088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.5224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5256e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.5273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.5287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.5380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.5384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.5386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.5391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.5398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.5406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.5416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.5439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.5443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.5446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.5449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.5451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.5456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.5461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.5483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5488e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.5496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.5510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.5520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.5529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.5544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.5555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.5568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.5571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.5672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4461 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 917/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7970e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.7157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 2.7175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.6620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.6060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5308e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.5150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.5011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.4985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.4969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.5068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.5209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.5236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.5249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 2.5248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - 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2.5365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.5363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.5355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 2.5353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.5346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.5340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 2.5334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 2.5323e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4969e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.5011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.5036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 2.5048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 2.5057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 2.5069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 2.5075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.5080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.5224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4475 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 918/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.8589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 292ms/step - accuracy: 1.0000 - loss: 2.8293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 2.7787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 2.6728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 2.6052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.5516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.5165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.5042e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.5201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.5377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 2.5520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 2.5639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.5702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.5739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.5753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.5743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.5726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.5689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.5653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.5619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.5576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 2.5544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.5527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.5508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 2.5479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 2.5370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.5349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.5326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 2.5315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.5300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.5285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.5270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.5263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 2.5214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 2.5142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 2.5043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5018e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 2.5004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 2.4931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.4871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.4814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.4744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.4727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.4709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.4689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.4670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.4652e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.4634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.4566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.4550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.4534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.4518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.4481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 2.4437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 2.4404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 2.4337e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4322e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 2.4316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4309e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 2.4294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.3434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4487 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 919/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.1395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.2229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4246e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.4108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3956e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.3433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.3334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.3236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.3056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.2834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.2833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.2832e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.2778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.2740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.2731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.2726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.2727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.2722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.2720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.2715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.2708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.2675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.2645e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.2636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.2629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.2617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.2618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.2614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.2613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.2617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.2623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.2629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.2633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.2635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.2636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.2636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.2636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.2634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.2632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.2628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.2626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.2622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.2615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.2607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.2604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.2626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.2626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.2621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.2387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4499 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 920/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.4129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.5210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.6325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.6054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.5552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.5025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.4003e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.3577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.3212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.2651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.2543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.2449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.2359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.2281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.2206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.2136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.2061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.1914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.1789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.1749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.1723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.1672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.1592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.1560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.1463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.1460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.1456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1456e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.1458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1455e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.1451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.1432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.1420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.1405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.1382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.1367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1362e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.1361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.1600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.1728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.1877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.1975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.2063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.2171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.2845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.6975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4470 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 921/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.2502e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.4454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.4171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.5491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5636e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.5528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.5606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.6433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.6789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.6879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.6981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.7087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.7452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7690e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.7910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.8180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.8454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.8900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9472e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.0043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.0093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.0140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.0186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.0229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.0271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.0310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.0350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.0388e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.0427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.0463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 3.0497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0529e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 3.0618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 3.0676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.0704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.0783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.0946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.0988e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1056e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1189e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1220e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.1302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.1324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.1347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4476 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 922/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.4922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.3440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.1732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.0398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.9037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.8863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.8889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.9192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9370e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.9401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.9464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.9573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.9623e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9433e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9327e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9228e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9069e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9049e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.8940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.8856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.8805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.8665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8644e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8284e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.8200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.8184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.8169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8125e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.8095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.8079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.8063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.8048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.8033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.8019e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.8004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.6321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4502 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 923/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.2619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.3449e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.4699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.4408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.4131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.3869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.3897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.3824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.3788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.3820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.3852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.3939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.3980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.3986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.3963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.3968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.3986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.3993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4002e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3998e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3994e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.3989e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.3971e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.3954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.3935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3902e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.3889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.3880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 2.3872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 2.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 2.3843e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 2.3835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3831e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 2.3818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 2.3804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 2.3789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 2.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.3757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.3737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.3154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4523 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 924/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.3082e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.0233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.8910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.7668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.6595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.5734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 2.5016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.4432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.3526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.2806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.2480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2194e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.2044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.2000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.1936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.1934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.1890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.1853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.1852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.1854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.1855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.1820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.1774e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.1738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.1716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.1699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.1679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.1673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.1667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.1656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.1638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.1632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.1629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.1625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.1621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.1615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.1611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.1601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.1598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.1595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.1592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.1588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.1585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.1580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.1576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.1571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.1565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.1561e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.1558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.1554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.1550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.1548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.1545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.1542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.1538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.1535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.1530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.1526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.1521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.1517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.1513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.1510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.1508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.1507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.1506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.1504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.1501e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.1499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.1496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.1493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.1489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.1486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.1483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.1080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4533 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 925/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.1929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.2539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0889e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0371e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.0216e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.0549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 2.0765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 2.0845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 2.0918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 2.0932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0924e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 2.0923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 2.0935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.0940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.0928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.0933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.0933e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.0914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.0906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.0898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.0890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.0882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.0875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.0869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.0865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.0858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.0853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.0848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.0841e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.0833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.0815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.0820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.0826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.0829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 2.0825e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.0821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.0821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.0813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0810e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.0806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.0801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.0796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.0794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.0798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.0805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0806e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 2.0807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 2.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 2.0837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4549 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 926/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.7947e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.9959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.2000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.0913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.0006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.9812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.9566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9521e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.9504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.9475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.9458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.9399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.9346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.9316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.9307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.9287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.9271e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.9244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9234e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9222e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.9208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.9171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.9138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.9137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.9144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.9152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.9158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.9159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.9166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.9171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.9180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.9186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.9182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9179e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.9173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.9165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.9157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.9148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9144e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.9133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.8819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4555 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 927/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.0953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.0554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.0000e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.9348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.9036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.8314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.8174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.7991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.7837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.7937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.7979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8008e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.8076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8099e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8208e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8334e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8355e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8376e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8509e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8578e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.7945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4570 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 928/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.6845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 1.6540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6473e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6404e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.6450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.6512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6657e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.6696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.6737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.6742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.6769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.6775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.6732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.6692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.6654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.6453e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.6418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6391e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.6379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.6320e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6326e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.6335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6358e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.6365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6372e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.6385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.6401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.6413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6425e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.6426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6427e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.6428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6428e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.6431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6436e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6444e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6446e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.6518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4572 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 929/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5447e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.5132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.4943e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4562e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.5112e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5282e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.5531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.5800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.5873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.5934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.5991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.6044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.6162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.6229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.6260e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.6280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6288e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.6294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.6306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.6324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.6339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6346e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.6347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.6341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6332e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.6329e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6325e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6317e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.6315e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.6303e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6293e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.6286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6279e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.6258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.6237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.6213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.6180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.6157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.6135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.6110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.6101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.6075e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6059e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.6052e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.6021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6014e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.6006e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.5999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5992e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5985e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.5978e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.5955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.5938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.5914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5203e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4590 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 930/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.4122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.5191e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.5044e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4498e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.4960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.4950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4931e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4848e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.4826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4820e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.4813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4814e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.4823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.4824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.4805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.4793e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.4791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.4785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.4781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.4772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.4768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.4768e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.4769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.4769e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.4765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.4756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.4751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.4742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.4725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.4712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.4700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.4681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.4668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.4655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4651e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4642e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.4637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.4623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.4608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.4590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4583e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.4579e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4575e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.4564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.4160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4608 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 931/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.4072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.3935e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.4081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.4071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.3928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.3667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.3696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3788e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.3815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.3847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3900e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.3905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.3911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3923e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3920e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3918e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.3915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.3907e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3905e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.3904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3903e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3901e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3893e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.3885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.3884e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.3880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.3878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.3874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.3869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3865e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.3863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3861e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.3860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3860e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.3857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3856e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3854e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.3851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.3646e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4623 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 932/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1316e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.1830e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1859e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.2174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2493e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.2650e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.2797e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2957e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.3264e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.3335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.3387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3416e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3411e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3407e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3399e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3239e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3227e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.3207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.3193e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.3186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.3177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.3164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.3158e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3155e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.3154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.3151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3151e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.3153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.3157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3164e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.3168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.3169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.3170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.3172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.3174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3178e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3180e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.3182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.3403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4636 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 933/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.3977e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.5506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.0231e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.4257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.5443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.5426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4914e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.4167e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.2423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.1799e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.1235e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.0722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.0224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.9733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.9266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.8406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.7218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.6219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.5917e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.5630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.5354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.5089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4593e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.4360e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4133e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3913e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.3128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.2952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.2451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.2140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.1845e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.1439e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.1070e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0728e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.0620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.0311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0120e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.0028e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.9678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.9434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.9210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.9141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.9007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.8942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.8753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.8516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8405e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.8352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8299e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8247e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.8195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.8043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7993e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7507e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7426e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7387e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7272e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7163e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.7068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.7012e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4654 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 934/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.1868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2481e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.2504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2450e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2045e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1984e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1959e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1915e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1932e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1916e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1881e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1873e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1850e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1813e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1776e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1767e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.1756e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.1730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.1704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.1689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.1655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1643e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.1634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.1621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.1609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.1606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.1607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.1600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.1601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.1604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.1604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.1604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.1603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.1601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1604e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1606e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4662 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 935/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.3447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 9.1918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1026e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.1243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1373e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.1678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1800e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.2034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.2161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.2152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.2173e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.2219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2276e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2305e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.2331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2350e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2374e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.2394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2410e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2424e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.2442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2466e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2489e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2541e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2551e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.2560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2566e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.2573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2576e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.2585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.2595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.2620e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2623e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.2628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2630e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.2629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2628e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.2624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.2614e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.2609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.2603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2601e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.2591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.2588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.2582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.2573e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.2570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2567e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 1.2559e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2555e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 1.2548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 1.2537e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 1.2519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.2504e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2494e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2470e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2454e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2448e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.2431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4677 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 936/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.9105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0550e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0170e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.0274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.0364e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.0512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0572e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.0596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.0595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.0595e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.0590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.0586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.0574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0696e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0823e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.0912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0930e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0973e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.0986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1001e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1043e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1080e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1114e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1118e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1128e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1130e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.1138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1137e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.1135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1135e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1134e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.1132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.1124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1122e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1113e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1108e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.1101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1095e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.1092e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.1084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.1074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.1064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1061e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1054e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.1051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4687 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 937/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.1225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0887e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.9343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.8931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.8427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.8224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.8120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.8274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.8262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.7870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7097e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.6867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.6795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.6751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.6540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.6552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.6547e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.6559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.6589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.6671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.6729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.6771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.6810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.6830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.6852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.6884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.6912e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.6942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.6975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.7005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.7186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.7222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.7263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 9.7294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 9.7356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 9.7442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.7464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.7479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 9.7489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.7494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.7493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 9.7492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.7490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.7497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.7500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 9.7501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.7503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.7508e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 9.7507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 9.7481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.7472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.7462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 9.7475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.7486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.7495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 9.7509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.7530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.7544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 9.7559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.7573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.7594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.7615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 9.7632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.7657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.7689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 9.7730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.7770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.7812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 9.7855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.7894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.7933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.7967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 9.8001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.8030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.8058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 9.8087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.8116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.8147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 9.8179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 9.8312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.8352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.8390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 9.8428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 9.8572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.0286e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4698 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 938/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.3386e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.0414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.9250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6421e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5840e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5339e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.4882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.4219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3960e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.3734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3553e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3397e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.3242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.3093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2948e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.2822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.2403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.2321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.2172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.2047e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1986e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.1613e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1568e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.1525e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1459e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1431e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1403e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1375e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.1291e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.1217e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1176e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.1136e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.1078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.1022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.1005e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0975e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.0962e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0949e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.0922e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.0882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.0836e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0789e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.0780e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0772e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.0747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.0726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.0706e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0685e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.0678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0659e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.0641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0629e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.0616e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.0596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0590e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.0577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.0552e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.0532e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0526e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0519e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.0506e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.7513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4711 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 939/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 8.5200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.0471e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.0356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0244e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0104e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0126e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0249e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0335e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.0361e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0396e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.0423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0438e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0462e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0486e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.0608e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0816e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0904e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.0981e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1053e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1119e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.1177e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.1229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1349e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1409e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.1435e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1478e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1515e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1531e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.1545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1556e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.1591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1600e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.1615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1624e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1632e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.1649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1656e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.1662e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.1666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.1672e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 1.1673e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 1.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.1678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.1679e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1677e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.1676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 1.1664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1683e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.1691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.1714e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.1735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1742e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.1746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1748e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.1753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.1760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.1760e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.1757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1755e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.1751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 1.1741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.1414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4716 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 940/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.6183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.8187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.9660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 9.8571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.7619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.7468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 9.6880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.6520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.6260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.6061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.6329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.6609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.7035e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.7488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.7734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.7976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.8083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 9.8128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.7931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.8970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.9968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0079e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.0255e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0343e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0417e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.0480e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0533e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.0618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.0649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0681e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.0741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0765e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0787e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.0807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0824e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.0858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.0868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0869e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0872e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.0874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0876e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.0874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0870e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.0849e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0838e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.0833e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.0815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0808e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0801e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.0794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0778e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0771e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.0764e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0759e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0754e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.0749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0730e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.0723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.0732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0736e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.0737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.0740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0739e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.0735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0733e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0731e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.0729e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.0721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.0713e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.0704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0701e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0698e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.0694e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.0680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0676e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0671e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.0667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0663e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0658e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0653e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.0649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.0084e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4734 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 941/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.8641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0152e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0423e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.9467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.7577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.6464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.5362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.4050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.3021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.2383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.1876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 9.1377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.0936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.0666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.1833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.2727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.3401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.3905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.4266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.4502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.4752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.4999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.5157e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.5438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.5668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.5862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.6030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.5882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.5839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.5799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.5448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.5371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.5289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 9.5087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.5029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.4039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.3985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.3943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.3893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.3845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3628e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.3474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.3448e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 9.3428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 9.3413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3332e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3202e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.3162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.3107e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 9.3085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 9.3037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.3022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.3005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 9.2987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.2971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.2953e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 9.2936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.2919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.2904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.2890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 9.2874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 9.2858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.2840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 9.2820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 9.2800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.2779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.2760e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 9.2739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 9.0189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4750 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 942/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.9846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.7645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.0702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.9629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.8704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.7859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.6788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.5962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.5312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.4887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.4674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 7.4632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.4639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.4840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.4881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.4908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.4888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.4954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.4997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.5042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.5092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.5262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.5384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.5501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.5695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.5873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.6067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.6240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.6423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.6605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.6762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.6891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.7013e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.7139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.7272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.7408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.7549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.7721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.7861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.7989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.8110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 7.8219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.8315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.8397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 7.8480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.8567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.8637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.8712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.8788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.8873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.8954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.9023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.9081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.9134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.9202e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.9262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.9321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.9380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.9430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.9482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.9531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.9580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.9620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.9656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.9691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.9722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.9750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.9777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.9805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.9833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.9863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.9927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.9988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.0103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.0239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.0386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.0418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.0451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.0483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.0516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.0543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.0567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0589e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.0673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.0702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.0734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.0762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.0795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.0832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.0869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.0903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.0935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.0967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.0996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.1022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.1048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.1071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.1094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.1114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.1133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.1151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.1171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.1188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.1204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.1217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.1230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.1246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.1260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.1273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.1287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.1299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 8.2752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4760 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 943/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.1413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 9.9652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 9.9833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.7710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 9.5376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 9.3567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.2045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 9.0411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 8.9469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.8709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.8181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.7764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 8.7331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 8.7110e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.7988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 8.8551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.8976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.9506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 8.9846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.0040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.0178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.0251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 9.0495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 9.0886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.1002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 9.0990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 9.0785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 9.0511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 9.0230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 9.0002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.9877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 8.9744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.9310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9190e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.9068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.8944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.8470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.8114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.7778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.7427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.7165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.7076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.6893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.6537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.6374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 8.6290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.6207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.6123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 8.6036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.5948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.5860e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.5772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 8.5687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.5605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.5530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 8.5458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.5389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.5317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 8.5245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.5173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.5099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.5024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 8.4947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.4872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.4802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 8.4732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.4667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.4603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 8.4541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.4477e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.4415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.4358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 8.4301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.4242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.4183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 8.4125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.4072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.4019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 8.3967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.3917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.3870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.3820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 8.3769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.3718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.3673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 8.3627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.3580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.3532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.3486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 8.3442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 7.8154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4770 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 944/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.5976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.9548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.4103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.3370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.1188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.9414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.7732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.5957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.4500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.3319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.2805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 8.2269e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.1917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.1610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.1334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.0937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.0532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 8.0149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.9832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.9493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.9242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 7.9010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.8812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.8613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 7.8437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.8376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.8328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 7.8235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.8123e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.8000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.7879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 7.7751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.7611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.7487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 7.7375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.7266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.7163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 7.7073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.6997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.6902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.6800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 7.6699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.6597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.6489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 7.6379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.6275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.6185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.6093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 7.6005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 7.5928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.5882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.5827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.5771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.5711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.5664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.5471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.5452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.5438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.5427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.5396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.5386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.5376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 7.5364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.5350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.5342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 7.5335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.5329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.5325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.5329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 7.5329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 7.5325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.5312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.5282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5278e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.5270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.5248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.5186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.5136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.5091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.5021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.5001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.4959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.4894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.4822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.4804e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.4784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.4765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4722e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 7.4709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 7.3217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4783 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 945/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.0324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.9637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.0684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.9229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.7796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.6952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.6167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.5276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.4555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.3923e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.3470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2975e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 7.2619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 7.2383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.2208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 7.1941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.1703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.1472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.1289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 7.1084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.0863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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7.0232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.0226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.0206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.0174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 7.0216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.0247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.0272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.0279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.0298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.0334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.0371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0401e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.0506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.0514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.0519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.0530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.0536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.0540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.0573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.0540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.0524e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.0505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.0487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.0464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.0441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.0422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.0362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.0344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.0322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.0299e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0156e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0136e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.0001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 6.9970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 6.9935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 6.9885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 6.9854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 6.9839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 6.9864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 6.9875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 6.9879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 6.9882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 6.9880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.9321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4800 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 946/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.9987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.4088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 8.4030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.1878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 8.0610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.9826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.8910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 7.7856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.6633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 7.5459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 7.4620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 7.4199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 7.3823e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 7.3637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 7.3674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 7.3597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 7.3492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 7.3337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 7.3185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 7.2996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 7.2753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 7.2518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 7.2329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 7.2165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 7.2037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 7.1921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.1850e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.1761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 7.1675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.1582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.1487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.1386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 7.1550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.1697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.1873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 7.2010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.2130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 7.2243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.2346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.2608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.2720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.2755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.2798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 7.2841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.2890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.2939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.2970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 7.2996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.3016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.3031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 7.3036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.3036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.3044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 7.3062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.3072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.3081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.3094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 7.3103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.3107e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.3104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.3108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.3117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.3203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.3284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.3362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.3435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.3504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.3567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.3633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.3696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.3751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.3799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.3844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.3885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.3922e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.3956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.3988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.4023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.4055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.4084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.4115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.4145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.4172e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.4194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.4212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.4227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.4239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.4249e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.4261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.4273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.4283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.4294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.4305e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.4316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.4324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.4337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.4347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.4355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.4359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.4363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.4369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.4374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.4379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.4381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.4384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.4385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.4385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.4383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.4379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.4376e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.4371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.4364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.4356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.4348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.4338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 7.3205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4819 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 947/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 7.4845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.6781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.6045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.3456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.1540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.8690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.7634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.5206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.4941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.4742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.4574e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.4450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.4251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.4016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.3770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.2960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2751e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.2625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 6.3307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.3907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.4439e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.4900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 6.5309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.5671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.6023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 6.6338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.6621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.6891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.7176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.7421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.7638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.7826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.8006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.8168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.8317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.8456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.8594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.8716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.8840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.8957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.9069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.9164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.9247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.9319e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.9391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.9452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.9500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.9541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.9578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.9730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.9778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.9824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.9866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.9905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.9939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.9969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.9997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.0023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.0054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.0080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.0143e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.0159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.0172e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.0181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.0187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.0191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.0191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.0197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.0202e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.0210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.0219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.0232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.0239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.0243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.0244e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.0242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.0237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.0229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.0220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.0211e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.0205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.0198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.0192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.0185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.0131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.0113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.0094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.0075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.0018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.0000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.9983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.9964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.9945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.9884e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.9799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.7235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4805 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 948/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.2653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.3617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 8.5613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.7901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 8.7681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.7229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.7397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 8.6996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.6169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.5438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 8.4733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.3957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.3239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.2622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.2093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.1899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.1721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 8.1622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.1486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.1312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.1128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 8.0962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.0813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.0622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 8.0434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.0271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 8.0116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.9942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.9772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.9586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9417e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.9049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.8689e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.8660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.8626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 7.8603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.8568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.8511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.8451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.8375e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.8285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.8188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 7.8093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.8002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.7908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 7.7827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.7746e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.7665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 7.7578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.7486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.7393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.7301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 7.7203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.7101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.6996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 7.6901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.6803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.6710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.6620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 7.6537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.6449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.6361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.6285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.6213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.6140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 7.6064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5862e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 7.5802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.5742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.5684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 7.5621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.5558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.5492e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 7.5425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 7.5164e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.5098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.5030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 7.4964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.4760e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.4495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.4304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.4243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.4182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.4131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.4081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.4031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.3985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.3939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.3891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.3843e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.3797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.3752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.3708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.3663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.3620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.3580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.3538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.3495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.3452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.3408e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.3364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.3191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.8242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4829 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 949/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 8.4339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.7084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.3479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.0734e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.8346e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.6702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.3820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.2948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.2235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.1644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.1675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.1778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.1987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.2133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.2228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.2315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.2414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.2517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.2553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.2549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.2527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.2522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.2465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.1989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.1985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 6.1978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.1898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 6.1866e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.1847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.1818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 6.1793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.1775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.1753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 6.1737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 6.1647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1615e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 6.1542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 6.1424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 6.1303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 6.1215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 6.1121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 6.1010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.0982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.0955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 6.0927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.0898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.0874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 6.0848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.0821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.0793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.0768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 6.0747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 6.0727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0709e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.0693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.0648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.0581e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.0528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.0473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.0419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.0374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.0326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.0273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.0231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0205e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.0183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.8838e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4842 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 950/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 7.2242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.4021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 7.2875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 6.9857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.7287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.5144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 6.6338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.7052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.7616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.7896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.8365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.8292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.8133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.8026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.7599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.7493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.7412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.7326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.7264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.7213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.7135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.7040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.6936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.6809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.6672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.6536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.6457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.6392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.6320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.6264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.6199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.6161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.6014e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.5641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.5619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.5608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.5582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.5553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.5521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.5487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.5449e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.5528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.5598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.5613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.5614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.5613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.5619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.5621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.5619e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.5617e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.5655e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.5688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.5719e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.5744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.5767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.5789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.5808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.5828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.5847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.5883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.5887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.5877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.5877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.5888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.5874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5912e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.5929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.5990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.6585e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4848 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 951/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.0502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.2483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3237e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.3065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.2912e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.2839e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.2773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.2830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.3105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.3195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3273e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.3176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.3168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.3173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.3298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.3517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.3747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.3940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.4000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4048e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.4120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.4204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4291e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.4316e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.4372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.4382e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.4391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.4403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.4413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.4424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.4437e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.4450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.4479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.4479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.4476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.4473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.4474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.4475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.4473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.4481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.4487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.4490e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.4496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.4484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.4481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.4478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.4481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.4495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.4506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.4515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4533e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.4537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4540e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.4550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.4577e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.4624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4703e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.6111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4868 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 952/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.3612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.3379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.6098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.5657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.4695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 5.3665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.2801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.2058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.1339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 5.0896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.0977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.1468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.1611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.1688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.1811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 5.1854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.1851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 5.1821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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5.2219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.2549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 5.2852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 5.3103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 5.3315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.3503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 5.3679e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 5.3842e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 5.5585e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.5595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.5608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 5.5620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.5633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.5647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.5657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 5.5667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.5676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.5688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.5698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.5710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.5721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.5732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.5740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.5747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.5752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.5755e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.5757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.5758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.5759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.5760e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.5761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.5762e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.5888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.5995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.9584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4883 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 953/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.2903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 6.4168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 7.0685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.0828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 6.9657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.8344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.6999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.5771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 6.4603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.3087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 6.1001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 6.0738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.0446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.9846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.9593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9350e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.8934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.8752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.8584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.8418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.8242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.8080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.7932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.7778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.7634e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.7512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.7385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.7259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.7213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.7168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.7105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.7036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.6965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.6895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.6827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.6757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.6685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.6625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.6558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.6493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.6428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.6368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.6303e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.6235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.6162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.6088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.6016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.5944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.5872e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.5803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.5729e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.5661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.5592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.5529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.5462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.5395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.5329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.5263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.5197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.5129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.5063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.4876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.4819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.4764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.4707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.4651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.4593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.4549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4418e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.4380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.4271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.4174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.4056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.4027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.3999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.3973e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.3947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.3926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.3904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3882e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3842e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.3801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.3780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.3758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.3737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.3716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.3695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3677e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3641e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.3601e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.3580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.3559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 5.0668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4894 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 954/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.1047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.9982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.9927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.8785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.8122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.7760e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.9881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.1152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.1956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.2529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.3012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 5.3789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.4659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.5911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 5.6828e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 5.7525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.8513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.8818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.9230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.9364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.9473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.9544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.9594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.9650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.9661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 5.9643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.9361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.9308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.9251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.9192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.9145e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 5.9105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9054e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.9007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 5.8911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8795e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 5.8741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 5.8588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8487e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 5.8366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.8334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.8298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.8256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.8209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.8162e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.8117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.8027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.7962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.7858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7819e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.7743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.7604e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.7507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.7396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.7236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.7032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.7001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.6910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.6850e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 5.6821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.6792e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.6764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 5.6736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.6706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.6676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.6645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 5.6614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.6582e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.6548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 5.6516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.6486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.6454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.6428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.6326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.6300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.6277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.6252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.6155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.3265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4903 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 955/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.2458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.2411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.3720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 5.2824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.2046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.1282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.0636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.9989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.8982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8878e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.8894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.9021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.9051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.9016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.8978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.8911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.8658e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.8403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8378e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8327e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.8271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.8129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.8025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.8005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.7993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.7980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.7925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.7893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7874e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.7886e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7891e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.7892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.7902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.7914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.7916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.7905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.7899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.7905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.7916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.7932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 4.7955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 4.7996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.8005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.8018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 4.8028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8035e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8049e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 4.8060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.8068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.8075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 4.8079e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.8081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.8082e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 4.8085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.8092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.8099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.8105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 4.8111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.8128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.8254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 4.8377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.8494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.8606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.8714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 4.8817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.8915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.9100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.9358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.9665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.9863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.9988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 5.0050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.0287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.0341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.0396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.0454e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.0509e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.0564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.0620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.0673e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.7045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4917 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 956/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.0796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.1475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.5535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 35s 306ms/step - accuracy: 1.0000 - loss: 4.5881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 304ms/step - accuracy: 1.0000 - loss: 4.5534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 304ms/step - accuracy: 1.0000 - loss: 4.6144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 303ms/step - accuracy: 1.0000 - loss: 4.6667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 4.6818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 302ms/step - accuracy: 1.0000 - loss: 4.6963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 4.7253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 302ms/step - accuracy: 1.0000 - loss: 4.7711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 4.8052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 4.8338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 4.8600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 4.8735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 4.8818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.8868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 4.8840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.8799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.8796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.8825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.8854e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.8887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 4.8930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.9024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.9091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.9140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.9166e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9217e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.9263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.9324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.9364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.9336e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.9631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.9896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.0149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0626e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.0841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1044e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.1562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1837e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.2060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.2155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.2247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.2338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.2423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.2504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.2578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.2647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.2712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.2775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.2833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.2888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.2939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.2986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.3032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.3073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.3114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.3153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.3192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.3227e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.3261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.3292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.3324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.3353e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.3379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.3402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.3424e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.3444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.3462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.3478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.3495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.3507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.3517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.3525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.3532e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.3536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.3539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.3541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.3542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.3544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.3548e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.3550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 5.3554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3555e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 5.3580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.3603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.3623e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 5.3642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.3664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.3685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 5.3704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 5.3776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.3789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.3801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 5.3812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3823e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 5.3849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 5.4757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4929 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 957/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.4125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.5686e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 5.7113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.5627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 5.3985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 5.2394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.1279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 5.0666e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 5.0068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.9665e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.0372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.1343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 5.2045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.2688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 5.3263e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 5.3685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.3982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 5.4397e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.4483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.4501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 5.4518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 5.4523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4514e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 5.4516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.4486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.4466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.4402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.4341e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.4261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.4175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.4077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 5.3971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.3873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.3786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 5.3693e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.3606e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.3517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 5.3426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.3326e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.3223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.3119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 5.3009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.2895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.2783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 5.2676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.2576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.2482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 5.2390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.2300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.2214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.2124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 5.2029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 5.1823e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.1759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.1704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 5.1651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1596e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1539e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1484e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 5.1432e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.1384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.1336e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 5.1285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.1232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.1179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 5.1127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.1075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.1027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.0982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 5.0938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.0897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.0855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 5.0811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.0765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.0717e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 5.0670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.0624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.0576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.0534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 5.0493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.0450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.0410e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 5.0371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.0333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.0293e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 5.0253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.0216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.0178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.0138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 5.0098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.0059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 5.0021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 4.9984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.9947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.9914e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 4.9883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 4.9757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 4.9657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.9564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.9441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.9409e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.9377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.9345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.9221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 4.5836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4929 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4993 Epoch 958/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.7954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.3868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.5320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.8852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.0610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.1816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.4362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.4789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.5390e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.5535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.5513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.5493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.5446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.5356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.5236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.5134e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.5091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.5075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.5086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5071e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 4.5050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.5025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.4985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.4935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.4885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.4830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.4789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 4.4753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.4737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.4721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 4.4706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4752e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 4.4768e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 4.4802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4807e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 4.4829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 4.4877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 4.4887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.4885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.4883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 4.4890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 4.4928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 4.4940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 4.4937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 4.4926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 4.4943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.4951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.4962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.4968e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4966e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.4963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4950e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.4938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.4934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.4929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.4924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.4920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.4918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.4901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.4896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.4889e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.4883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.4865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.4847e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.4841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.4834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.4827e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4811e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.4799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.4004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4943 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 959/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.1970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.8982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.7116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.5221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.2982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2402e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1562e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.1444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1672e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.1911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 4.2168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.2422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.2696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 4.3016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3398e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 4.3643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 4.3761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - 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4.4415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.4435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.4438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 4.4442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.4431e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.4436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.4445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.4456e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.4507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.4489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.4467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.4452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.4464e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.4488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4473e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.4461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.4446e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.4494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.4502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.4502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.4500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.4499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.4485e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.4467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 4.4451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.4429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.4412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.4395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.3908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4951 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 960/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 4.5947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 4.6446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 4.7904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 4.6739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 293ms/step - accuracy: 1.0000 - loss: 4.7208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.6949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 4.6510e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.6081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.5632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.5264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 4.5029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 4.4939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 4.4806e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 4.4696e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 294ms/step - accuracy: 1.0000 - loss: 4.4605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 4.4535e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.4520e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 4.4495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 4.4312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 4.4171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.4011e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.3947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.3894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 4.3826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.3749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.3691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 4.3671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.3647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.3654e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.3652e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 4.3653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.3642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.3642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 4.3636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.3633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.3624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 4.3610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 4.3585e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3578e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 4.3583e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 4.3546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 4.3486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.3465e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.3450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 4.3436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.3426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.3411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.3394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.3364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.3345e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.3324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.3302e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.3284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.3262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.3242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.3223e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.3203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.3181e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.3158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.3201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.3240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.3396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.3429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.3462e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.3493e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.3526e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.3558e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.3588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3644e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.3690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.3711e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.3731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.3749e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.3766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.3784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 4.3801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3846e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 4.3857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.3867e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 4.3875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.3881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.3899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.3919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.3926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.3932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.3936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.3952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.4310e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4970 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 961/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.0949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 4.4810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.4885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.4232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.2951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1594e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0434e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 4.0457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0456e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0468e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0515e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0521e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0529e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0483e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0505e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0549e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0560e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0655e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0676e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0765e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.0876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.1007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.1016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.1024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.1033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.1040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.1046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.1052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.1059e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 4.1068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.1077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.1085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.1093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 4.1100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.1106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.1111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 4.1116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.1120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.1124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.1128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 4.1131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.1537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4984 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 962/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 3.4699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.1790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 4.4236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3308e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.2602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1531e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.1053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0620e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0486e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.0343e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.0119e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0027e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 4.0037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.9978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.0074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.0135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.0160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.0282e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 4.0427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0469e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 4.0534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0538e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0534e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 4.0523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0513e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 4.0494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0482e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 4.0426e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 4.0395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 4.0384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 4.0373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 4.0332e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 4.0321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0318e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 4.0311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 4.0284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0270e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 4.0235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0286e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0307e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 4.0328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0404e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.0438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0471e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.0499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 4.0525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 4.0550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0571e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.0614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.0668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.0687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.0705e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 4.0720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0748e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0759e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.0769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0781e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.0808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.0849e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0861e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0880e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0890e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0913e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 4.0930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 4.0944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 4.0947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0944e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 4.0940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 4.0695e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4994 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 963/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6801e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.8902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.9275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.8663e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.7502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.6856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.7664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.7896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.8333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8561e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.8655e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8690e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.8694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.8698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.8702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8714e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8495e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8459e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.8387e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8354e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.8329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8266e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.8192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.8092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.8060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.8036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.8009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7864e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.7774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.7761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7753e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7741e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.7730e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7708e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7700e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.7692e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.7680e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.7662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7657e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7648e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.7645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7642e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.7639e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.7636e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.7633e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7628e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7622e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7616e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7610e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.7605e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.7603e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.7602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7598e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7595e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.7584e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.7580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.7576e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.7572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.7569e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.7566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.7564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.7566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.7566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.7566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.7565e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7556e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.7552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.7040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5001 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 964/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 305ms/step - accuracy: 1.0000 - loss: 4.3830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.4943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.3912e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.2113e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.0770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.9699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.8165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.7614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.7151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6807e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 3.6182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.6029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 3.5883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5824e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.5637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5522e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 3.5370e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5333e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 3.5245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.5246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5250e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 3.5216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.5167e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.5135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.5107e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4973e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4993e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4999e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.4997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.4978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.4963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4958e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.4948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.4920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.4899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.4905e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.4902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4898e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.4895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4901e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4926e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4928e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4930e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.4933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4932e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.4924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.4924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4929e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4935e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4943e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.4949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4956e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.4969e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4990e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.5625e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5009 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 965/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.7114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.6407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.5674e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.4575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.3592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.2892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2335e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.1425e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.1185e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 3.1057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 301ms/step - accuracy: 1.0000 - loss: 3.1007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 3.1104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 3.1221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 301ms/step - accuracy: 1.0000 - loss: 3.1260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 3.1262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 3.1294e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 3.1290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 301ms/step - accuracy: 1.0000 - loss: 3.1254e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 3.1218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 3.1178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 301ms/step - accuracy: 1.0000 - loss: 3.1176e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 3.1165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 3.1175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 301ms/step - accuracy: 1.0000 - loss: 3.1193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 3.1218e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 3.1236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 3.1260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 301ms/step - accuracy: 1.0000 - loss: 3.1276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1285e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.1283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.1289e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.1298e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.1322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.1348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.1373e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.1394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.1413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1427e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.1444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1494e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.1512e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.1530e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.1544e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.1553e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.1559e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.1563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.1564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.1567e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.1573e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.1579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1587e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1602e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 3.1637e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.1650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.1661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 3.1669e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1675e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1681e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.1687e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1713e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.1720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.1756e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1767e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.1791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1802e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1816e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1829e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1844e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1871e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1896e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.1948e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1954e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1983e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.1998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.2019e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2024e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.2032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2042e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.2045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2065e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 3.2137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5027 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 966/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 5.1701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 4.6272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.3724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.1020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.9094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.7479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.6393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.5489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.4678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.4144e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 3.3782e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3419e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.3192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.3200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 3.3296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.3496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 3.3624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.3704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.3737e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 3.3771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.4618e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4632e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.4659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4670e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4682e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.4698e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.4895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.4955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5013e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.5068e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5169e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.5262e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5306e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.5389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.5504e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5542e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5579e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.5647e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.5735e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.5918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.1050e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5049 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 967/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.6132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.9964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 4.0369e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.0661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 4.0214e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 3.9496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.8850e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.8470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.8001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.7586e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.7231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 3.6832e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.6467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 3.6133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 3.5870e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 3.5180e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4970e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4546e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 3.4413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 3.4421e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.4430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.4440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4475e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4467e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4457e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4438e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4315e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.4313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.4323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 3.4334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4337e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 3.4336e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.4332e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4323e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.4311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4292e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 3.4406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4516e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4627e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 3.4727e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4826e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.4920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 3.5007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5361e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.5527e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5600e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.5731e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5789e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.5927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.6099e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6199e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6389e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 3.6479e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6564e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6645e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 3.6721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6792e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 3.6924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.6984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.7039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.7090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 3.7140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 3.7281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.7324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.7363e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 3.7399e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7433e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7491e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 3.7518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.7543e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.7568e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 3.7591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7612e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7631e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.7650e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.7684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7699e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.7712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7723e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.7742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7750e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.7764e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7778e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7783e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.7788e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.7803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5016 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 968/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.7980e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.6798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.7452e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.6797e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.5796e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.4808e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 3.4135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.3656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 3.3111e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.2685e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.2476e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.2336e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.2193e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.2100e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1988e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.1869e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1743e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.1461e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1352e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1141e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.1060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0754e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.0608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0537e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0466e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0405e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.0339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 3.0168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0117e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0083e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 3.0028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.0002e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 2.9949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 2.9876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 2.9814e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9807e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 2.9793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 2.9777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 2.9772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9769e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 2.9771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9773e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9776e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 2.9777e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.9798e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.9818e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.9836e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 2.9855e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.9873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.9889e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 2.9903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.9920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.9940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 2.9957e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.9972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.9985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.9996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 3.0006e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.0015e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.0025e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 3.0036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.0046e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.0056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 3.0067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0077e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 3.0093e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0101e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 3.0120e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.0139e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0146e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.0163e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0171e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0183e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.0187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.0200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0204e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.0212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0220e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0225e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.0233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.0257e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0264e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0277e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0284e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.1033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5061 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 969/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 2.8900e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.3653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2965e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.2161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 3.1394e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 3.0314e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 3.0001e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9701e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.9557e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 2.9403e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9234e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9210e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 2.9279e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9272e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9233e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9235e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9228e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.9280e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9349e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9450e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9500e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9503e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9502e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9511e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.9563e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9653e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.9691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9721e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.9772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.9859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9876e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9897e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.9916e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.9940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.9961e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.9977e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.9995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.0037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0051e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 3.0056e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0061e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.0067e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 3.0075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.0076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0081e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.0085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0085e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0087e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.0089e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0096e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.0121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0128e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.0132e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0135e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0138e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.0142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.0154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0155e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.0154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.0151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0151e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.0152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0153e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0154e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0168e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0186e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0201e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0224e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0238e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0248e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0259e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0268e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.1253e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5063 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 970/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.5902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.9629e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0489e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.9992e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.9281e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.8106e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.7744e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7430e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.7942e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.8147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.8313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8413e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8488e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.8845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9152e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9597e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.9774e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.0023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.0242e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.0460e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.0668e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.0877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.1245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1506e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.1611e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1761e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.1821e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1921e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.1966e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.2010e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.2080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2092e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.2107e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2124e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2130e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2122e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2118e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2112e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2103e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2094e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2070e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.2062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2053e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.2033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2021e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.2013e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.2003e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1981e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1976e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1972e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1967e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1960e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1952e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.1945e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 3.1919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1920e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1919e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 3.1918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1917e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 3.1912e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1911e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1908e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1904e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 3.1899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1893e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1888e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 3.1885e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1881e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1877e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 3.1873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1863e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1856e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.1848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1840e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1831e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.1822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1813e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1803e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.1794e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1775e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1766e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.1757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.1728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1707e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.1697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.1691e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 3.0995e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5072 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 971/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.1643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 3.2940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.2738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.1704e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 3.0716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.9936e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9334e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9624e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.9817e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0036e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0331e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 3.0444e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0552e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0710e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 3.0791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0809e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0800e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 3.0834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0857e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0883e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 3.0909e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.0962e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1012e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 3.1073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1142e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1191e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 3.1236e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1240e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 3.1431e-09 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.2590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.2694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 3.2787e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.2868e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.2938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 3.2998e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.3055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.3104e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 3.3147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.3184e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.3219e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.3245e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 3.3274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.3301e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.3322e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 3.3339e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 3.3348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3371e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3381e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3393e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3391e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.3388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.3386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.3380e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.3372e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3368e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3360e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.3355e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3351e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.3344e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3329e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.3321e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3311e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3287e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.3276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3261e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3251e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.3239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3230e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3226e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.3221e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3216e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3212e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.3200e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3195e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3189e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.3182e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3173e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3165e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.3157e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.3147e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3126e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.3115e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3102e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3088e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.3074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3060e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3045e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.3030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.3007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2996e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.2985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2974e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2963e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.2951e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.2927e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.2902e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.1348e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5076 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 972/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 3.0297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 3.0519e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.9851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.8907e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.8271e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7678e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.7388e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.7267e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7377e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.7791e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 2.8550e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8724e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8859e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.8938e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9023e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 2.9004e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8997e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8989e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 2.8985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9007e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.9016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.9009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.8986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.8959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8892e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8875e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.8848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.8825e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.8810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.8799e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.8780e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.8757e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.8740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8720e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8697e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8671e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.8643e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.8621e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.8599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 2.8575e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.8551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.8528e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.8501e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8445e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8416e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 2.8386e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 2.8358e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.8300e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8275e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.8213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8192e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8170e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.8149e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8129e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8107e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.8086e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8063e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8043e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8037e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.8033e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.8032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.8032e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.8030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.8030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.8029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.8029e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8028e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8026e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.8052e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8066e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.8095e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8114e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8137e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.8159e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8198e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8215e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.8231e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8260e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.8276e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8304e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.8317e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8328e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8338e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.8356e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8366e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8374e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.8383e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8392e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.8407e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8415e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8422e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.8436e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.8441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.8446e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.8451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8455e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8463e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8472e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.8480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.9458e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5075 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 973/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.4222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.4931e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 2.5497e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 3.7203e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 4.2312e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.4742e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.5804e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 4.6127e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.5934e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.5593e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.5174e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.4664e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.4121e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 4.3608e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.3140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.2640e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.2179e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.1726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.1265e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0815e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 4.0379e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - 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3.7630e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.7090e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6833e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6588e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6357e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.6140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.5933e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.5747e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.5572e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.5414e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5255e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.5116e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4978e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.4845e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4592e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.4470e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4359e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.4140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.4038e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3947e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3858e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.3771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3688e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3607e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.3525e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3443e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.3283e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3208e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3140e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3076e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.3018e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2959e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2899e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.2841e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2784e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2726e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.2667e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2554e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2499e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.2447e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2396e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2347e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.2296e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2247e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2197e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.2148e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.2000e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.1955e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1910e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1865e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.1822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1779e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1736e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.1694e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1651e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1609e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.1523e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1480e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1440e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.1400e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.1362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1325e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 3.1290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1256e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1222e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1188e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 3.1160e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1133e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1105e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 3.1080e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1055e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1031e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 3.1008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.0985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.0964e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.0941e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 3.0918e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.0895e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.0873e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 3.0852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 3.0831e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0810e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0790e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.0770e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.8411e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5092 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 974/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 6.0728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.2342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 5.1852e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 4.9309e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.7039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.4940e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.3362e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 4.2057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.0812e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.9161e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 3.8771e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8428e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.8084e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 3.7758e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7435e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 3.7107e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.6805e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 3.6507e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.6229e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5939e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 3.5659e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5395e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5206e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 3.5020e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4738e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 3.4613e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 3.4384e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4290e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 3.4194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4098e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 3.4009e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 3.3924e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.3834e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.3745e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.3660e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 3.3580e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.3496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.3412e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 3.3324e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.3239e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.3158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 3.3072e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 3.2985e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.2903e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.2820e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 3.2739e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 3.2661e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2591e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 3.2517e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2442e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2365e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 3.2288e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2209e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2131e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.2057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 3.1986e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1915e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1848e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 3.1785e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1725e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1599e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 3.1536e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1478e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1423e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 3.1367e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1313e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1258e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 3.1207e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1109e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1064e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 3.1017e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0971e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0925e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 3.0879e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0851e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0822e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 296ms/step - accuracy: 1.0000 - loss: 3.0793e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0733e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0706e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 3.0684e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0662e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0638e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 3.0614e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0590e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0566e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 3.0541e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0518e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0496e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0474e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 3.0451e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.0429e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.0406e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 3.0385e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.0364e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.0342e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 3.0320e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 3.0297e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0274e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0252e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.0232e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0213e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0194e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.0175e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0158e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0141e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0125e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.0108e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0091e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0074e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.0057e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0039e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0022e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.0005e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 2.9987e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.7949e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.5106 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4994 Epoch 975/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 296ms/step - accuracy: 1.0000 - loss: 2.4178e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 2.6330e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.5950e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 2.4906e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.4246e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.4040e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 2.3991e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 2.4075e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4069e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4078e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.4073e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.4062e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4016e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4030e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.4058e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 2.4047e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.4008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3982e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 2.3946e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.3894e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.3830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 2.3786e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3763e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3740e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 2.3728e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 2.3716e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3712e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3718e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 2.3715e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3702e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3772e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.3830e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3887e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3937e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.3984e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.3097e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.1481e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9243e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.6441e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.3187e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 6.9551e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 7.6008e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.3656e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.0835e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 9.9732e-09 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.0819e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.1666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.4500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.7174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.9705e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.2102e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 2.4634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 3.5229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.5434e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.5094e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 6.4312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 8.0897e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.0476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.3455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.7548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 3.1576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.5005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 6.2675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 8.9740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.2561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 1.7136e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.2568e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.8550e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 3.5707e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.5561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 5.7772e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.2109e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 9.2982e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.1626e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.4167e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 1.6852e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 1.9796e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.3099e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.6654e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.0424e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.4411e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.8575e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.2944e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.7492e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.2340e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.7242e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.2204e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.7189e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 7.2266e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 7.7326e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.2440e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.7600e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 9.2762e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 9.7892e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0299e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.0806e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1309e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.1806e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2295e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2776e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3251e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.3719e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4176e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4623e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5060e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5486e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5902e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6310e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.6709e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7099e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9999 - loss: 1.7480e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9999 - loss: 1.7852e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 0.9999 - loss: 1.8217e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9999 - loss: 1.8572e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9999 - loss: 1.8917e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 0.9999 - loss: 1.9254e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 1.9581e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 1.9901e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 2.0212e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.9999 - loss: 2.0515e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 0.9998 - loss: 5.6597e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999 - val_accuracy: 0.9766 - val_loss: 0.3781 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4989 Epoch 976/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 0.9999 - loss: 1.5502e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 0.9999 - loss: 1.6928e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 0.9999 - loss: 1.6047e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.5191e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.4465e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.3767e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.3177e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.2654e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.2177e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1858e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1561e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.1282e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.1019e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0789e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4999  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.0570e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0357e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 1.0148e-04 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.9455e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.7490e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.5590e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.3768e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.2141e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 9.0564e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.9036e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.7563e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.6146e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.4780e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.3457e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.2176e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.0940e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.9742e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 7.8579e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.7453e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.6362e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 7.5306e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 7.4281e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.3288e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.2326e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 7.1392e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 7.0485e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.9603e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.8745e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7911e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7100e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.6309e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.5540e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4792e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.4063e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.3353e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.2662e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.1989e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.1332e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0690e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0064e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.9452e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.8855e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.8271e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7701e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7144e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6599e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6066e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5545e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.5036e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4537e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.4048e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.3570e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.3101e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2642e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.2191e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.1750e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.1317e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0893e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.0477e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.0068e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.9668e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 4.9275e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8889e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8510e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 4.8138e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7772e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7413e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.7060e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 4.6714e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6373e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.6038e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 4.5708e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5384e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.5066e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 4.4753e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4444e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.4141e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3842e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 4.3548e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.3259e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.2974e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 4.2693e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.2417e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.2145e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 4.1877e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.1613e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.1353e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.1096e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 4.0843e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0594e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0349e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 4.0106e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9868e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9632e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.9400e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.9171e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8945e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8722e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.8502e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8285e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.8071e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.7859e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7650e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7444e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7241e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.7040e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3128e-05 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3696 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 977/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.6931e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.7322e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7102e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6736e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6359e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.6019e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5716e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.5449e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5264e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5133e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.5002e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.4925e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.4875e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4832e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.4773e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4709e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4651e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.4592e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4530e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4462e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.4399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4345e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4288e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4236e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.4194e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4160e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4122e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.4081e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.4039e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3951e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.3906e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3863e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3823e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.3783e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3747e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.3687e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3656e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3624e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3593e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.3561e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3528e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3496e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.3464e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3435e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3405e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.3377e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3350e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3325e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3299e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.3274e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3224e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.3199e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3173e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3148e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.3123e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3098e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3074e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3052e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.3030e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.3008e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2997e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.2986e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2974e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2962e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.2949e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2936e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2924e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2911e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.2898e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2885e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2873e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.2861e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2848e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2835e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.2821e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2808e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2794e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2781e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.2768e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2754e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2741e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.2729e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2717e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2704e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.2692e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2679e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2666e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2653e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2640e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2629e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2618e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 1.2607e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2596e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2585e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 1.2575e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2564e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2553e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2542e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 1.2531e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2519e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2508e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 1.2497e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2486e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2474e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 1.2463e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2453e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2442e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2431e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 1.2420e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 1.2409e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2387e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2375e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2364e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2352e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2341e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.0976e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3671 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 978/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1226e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1412e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.1399e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.1017e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.0711e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.0436e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.0192e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.9761e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.8074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.6654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.5546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 9.4423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 9.3269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 9.2992e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.2604e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.2178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.1773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 9.1342e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.0912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.0481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 9.0092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.9801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.9497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 8.9025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.8850e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.8636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 8.8411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.8193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.7967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 8.7752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.7095e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 8.6891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.6714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.6558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 8.6434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6167e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 8.6031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.5893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.5751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 8.5601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.5456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.5319e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.5176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 8.5037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4791e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 8.4548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 8.4168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.4036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 8.3780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3532e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 8.3309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.3081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 8.2967e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 8.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2386e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 8.2159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.2054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 8.1845e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 8.1525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 8.1099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0993e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0890e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 8.0792e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0696e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 8.0499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0303e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0205e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 8.0105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 8.0008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.9913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 7.9818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.9726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.9638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 7.9553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 7.9212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.9043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 7.8958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.8874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.8790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 7.8707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 7.8389e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8227e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 7.8148e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.8068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.7989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.7911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 7.7833e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.8613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3769 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4990 Epoch 979/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.5853e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.5438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.5449e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 7.2880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.1383e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 6.9815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.8279e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.6905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.5592e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.3234e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.2749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.2192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 6.1894e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1595e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 6.1191e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0785e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 6.0420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 6.0080e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 5.9955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 5.9616e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9485e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 5.9056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.8932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.8819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 5.8704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.8599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.8512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 5.8454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.8388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.8323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 5.8255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.8183e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.8106e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.8022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 5.7940e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.7863e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.7781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 5.7707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.7641e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.7579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.7516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.7451e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.7384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.7318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 5.7249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7178e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 5.7044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6978e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6851e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.6794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6733e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.6610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6548e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.6424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6308e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6251e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.6198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.6046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5994e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5962e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.5927e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5819e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.5784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.5676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.5574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5385e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.5306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5229e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.5194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.5012e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4975e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.4938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4867e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.4831e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4728e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.4694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4701e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.4711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4715e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.4716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.4684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3865 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 980/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.4780e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2712e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.1410e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.0398e-06 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 9.6874e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 9.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 8.7050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 8.3296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 8.0071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.7421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 294ms/step - accuracy: 1.0000 - loss: 7.5270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.3360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.1766e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 7.0454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.9357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.8321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.7376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 6.6495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 6.5656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 6.4855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 6.4092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - 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5.9878e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.9493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.9121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 5.8763e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.8413e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.8079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 296ms/step - accuracy: 1.0000 - loss: 5.7769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 5.7468e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0418e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0346e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0209e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.0071e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.0003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9937e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.9871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9744e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.9683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9498e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.9436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.9254e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.9197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.9140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 4.9087e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.9034e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 4.8982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8929e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 4.8877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8824e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8771e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 4.8718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8560e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 4.8508e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 4.2307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3855 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 981/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.8804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.8721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 4.8036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.6711e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 4.5424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 4.4483e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.3573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2727e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 4.2008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 4.1454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.1125e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 4.0846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0540e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0475e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 4.0350e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0219e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 4.0074e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 3.9923e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 3.9472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9257e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.9077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.9003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.8832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.8499e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 3.8242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8168e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 3.8045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7925e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 3.7804e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.7740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.7674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 3.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.7551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.7490e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 3.7434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7281e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 3.7228e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7174e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 3.7062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.7004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.6947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 3.6892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.6839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.6788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.6740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 3.6694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.6648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.6603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 3.6557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.6511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.6465e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 3.6421e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6377e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6295e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 3.6256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6220e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6187e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 3.6153e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 3.6050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.6016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 3.5913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 3.5817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 3.5729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 3.5609e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.5577e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.5551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 3.5523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5471e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 3.5447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5422e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 3.5348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 3.5272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 3.5199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5176e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5131e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 3.5108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 3.5039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.5015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 3.4944e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 3.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3877 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 982/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 3.0479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.2746e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 3.3189e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 3.2424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 3.1617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 3.1079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 3.0288e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 2.9913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9551e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 2.9420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9341e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 2.9653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 2.9698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 2.9735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.9642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9625e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.9644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.9614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9587e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 2.9572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 2.9559e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 2.9513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.9447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9429e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.9391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9375e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.9327e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9292e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.9213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 2.9139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 2.9084e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 2.9024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.9003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 2.8947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8914e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 2.8898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8882e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 2.8865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 2.8848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8815e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8799e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 2.8784e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 2.8754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 2.8739e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 2.8695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 2.8632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 2.8583e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 2.8539e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8525e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8511e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 2.8482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 2.8438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.8424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.8410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 2.8396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8382e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 2.8344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 2.8304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8277e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8264e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 2.8250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 2.6631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3910 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 983/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.6206e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 2.7780e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.8772e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 301ms/step - accuracy: 1.0000 - loss: 2.8691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8393e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 2.8024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.7621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.7208e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 2.6479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.6056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.5934e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5857e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 2.5798e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 2.5626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5247e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.5213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 2.5133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 2.5117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 2.5073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.5007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 2.4984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4972e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4963e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 2.4957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4946e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4932e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 2.4915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 2.4895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4875e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 2.4829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4809e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 2.4773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4749e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 2.4725e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4713e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 2.4683e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 2.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 2.4581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4569e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4558e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 2.4546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 2.4536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4576e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 2.4588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4611e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 2.4620e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 2.4639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4646e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.4651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.4656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.4669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.4675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 2.4675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4675e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.4674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.4672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4669e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.4664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.4651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4647e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.4638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4631e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.4622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4618e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.4608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4598e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4593e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.4588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 2.4019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.3944 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 984/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 2.2033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.4188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 2.3514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 2.3259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.3024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 2.2895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 2.2581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 2.2516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 2.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.2407e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 2.2440e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.2430e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.2402e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 2.2365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.2336e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.2293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 2.2239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2162e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2124e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 2.2092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.2068e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.2053e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 2.2035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.2011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.1984e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 2.1951e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 2.1859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.1844e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.1830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 296ms/step - accuracy: 1.0000 - loss: 2.1823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.1817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.1806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.1794e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 296ms/step - accuracy: 1.0000 - loss: 2.1781e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.1769e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.1754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 296ms/step - accuracy: 1.0000 - loss: 2.1737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.1721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.1707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.1691e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 296ms/step - accuracy: 1.0000 - loss: 2.1679e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.1668e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.1660e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 296ms/step - accuracy: 1.0000 - loss: 2.1648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.1635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.1623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 2.1610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 2.1594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1578e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 2.1546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1530e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 2.1502e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1491e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1479e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1466e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 2.1453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 2.1410e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1395e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 2.1368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1344e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 2.1321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1297e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 2.1284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1256e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 2.1242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 2.1230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1217e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 2.1192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 2.1157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1133e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 2.1108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 2.1078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 2.1051e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 2.1014e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.1005e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0995e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 2.0985e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0966e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 2.0957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0930e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 2.0921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0904e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 2.0895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0877e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 2.0859e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 1.9800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3967 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 985/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 2.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 2.1003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 2.1198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1391e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 2.1164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 2.0370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 2.0030e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9900e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.9788e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.9636e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.9580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9518e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.9457e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.9225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9181e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 1.9120e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9107e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 1.9059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.9009e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.8948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.8887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8895e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.8903e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.8912e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8916e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8918e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8919e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.8917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8911e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.8908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8906e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8902e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.8896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8891e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.8889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8881e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.8876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8868e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.8855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8848e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8842e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.8832e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8820e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.8813e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8796e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.8787e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8778e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8768e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.8764e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8759e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.8755e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8751e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8748e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.8745e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8740e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.8726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8721e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8716e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.8712e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.8699e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.8676e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8670e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8665e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 1.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 1.8644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8633e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8627e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 1.8621e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8615e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 1.8601e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 1.8581e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8574e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 1.8556e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8550e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8543e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 1.8536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8522e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 1.8507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 1.7640e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.3994 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 986/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 1.6284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.6735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.7454e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.8361e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.8378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8368e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8348e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.8331e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8330e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.8318e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8268e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.8242e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8207e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8179e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.8158e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8111e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.8096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8065e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.8020e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7991e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.7926e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7862e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.7828e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.7729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7703e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.7687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.7672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.7656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.7638e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.7599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7582e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.7563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.7534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7513e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.7503e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7493e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7484e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7476e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.7469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.7444e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7434e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.7411e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7399e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.7376e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.7364e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7354e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.7335e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7325e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.7304e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7293e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7271e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.7260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7250e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7239e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.7230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.7203e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7194e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7184e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.7165e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7155e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.7136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7118e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.7109e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7100e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.7072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7062e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.7042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.7015e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.7007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6998e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.6981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.6956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6931e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.6922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.5901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4014 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4991 Epoch 987/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.9157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.8655e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.8708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.8193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.7735e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.7323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.7453e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.7526e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.7467e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.7370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7355e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.7349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7305e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.7287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7259e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7221e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.7177e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - 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1.6889e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.6790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6753e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 1.6654e-07 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 1.5752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5743e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5734e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 1.5724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5705e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 1.5695e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5664e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5654e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5599e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5589e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5571e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5553e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5544e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5536e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5528e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5520e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.5504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5496e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.5481e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.5448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.4495e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4035 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 988/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 295ms/step - accuracy: 1.0000 - loss: 1.4501e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.4630e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 293ms/step - accuracy: 1.0000 - loss: 1.4981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 1.4632e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4317e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 1.4054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.3830e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.3650e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.3329e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3262e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.3210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.3231e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3253e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3265e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.3270e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3266e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3258e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3246e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.3233e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3225e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3215e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 296ms/step - accuracy: 1.0000 - loss: 1.3204e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3200e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 296ms/step - accuracy: 1.0000 - loss: 1.3192e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3186e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3180e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 296ms/step - accuracy: 1.0000 - loss: 1.3175e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.3170e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3157e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.3152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.3143e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3147e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 1.3146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3145e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 1.3141e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3138e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 1.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 1.3135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3134e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 1.3126e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3121e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 1.3113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3108e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3105e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 1.3103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3102e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 1.3097e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3096e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 1.3091e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 1.3083e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3079e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3077e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 1.3075e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3072e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 1.3066e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3063e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 1.3056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 1.3052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 296ms/step - accuracy: 1.0000 - loss: 1.3044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.3043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.3042e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 296ms/step - accuracy: 1.0000 - loss: 1.3041e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.3039e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.3037e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 296ms/step - accuracy: 1.0000 - loss: 1.3035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3033e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3031e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 296ms/step - accuracy: 1.0000 - loss: 1.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.3024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 296ms/step - accuracy: 1.0000 - loss: 1.3022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3022e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 296ms/step - accuracy: 1.0000 - loss: 1.3018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3016e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 1.3017e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3018e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3019e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 1.3021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3023e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3025e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 1.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 1.3028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3028e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3027e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 1.3026e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 300ms/step - accuracy: 1.0000 - loss: 1.2950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4054 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 989/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.2790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.2921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3086e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2893e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.3388e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3602e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.3682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.3661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 1.3534e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3474e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3436e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 1.3379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 1.3249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 1.7076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.0352e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.3197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 2.5662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.7800e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 2.9667e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 3.1306e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.2747e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.4007e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 3.5122e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 3.6103e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.6969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 3.7730e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.8405e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.9003e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 3.9537e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.0008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0427e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.0807e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.1149e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1447e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1708e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.1939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2139e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2312e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.2590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.2704e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.2797e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.2879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.2949e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.3008e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 4.3052e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3085e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3110e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 4.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.3136e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.3130e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 4.3117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.3098e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.3073e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 4.3046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.3013e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2982e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2947e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 4.2908e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2865e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 4.2765e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.2710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.2653e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.2594e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 4.2533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.2470e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 4.2338e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.2269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.2199e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 4.2127e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.2054e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.1980e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.1905e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 4.1829e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.1752e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.1674e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 4.1596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1517e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1438e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 4.1358e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1117e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 4.1035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.0953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 4.0871e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 4.0789e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0624e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 4.0542e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0460e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0378e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 4.0214e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0132e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 4.0050e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 3.9968e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.9887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.9805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 3.9724e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9643e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9482e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 3.9401e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.9321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.9241e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 3.9161e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.9081e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.9002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.8922e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 3.8843e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 2.9426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4054 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 990/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 1.5278e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.5808e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.6754e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.6584e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 1.6390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.6274e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.6193e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.6049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 1.5885e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5762e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5680e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 298ms/step - accuracy: 1.0000 - loss: 1.5710e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 1.5758e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.5795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.5841e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5858e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5935e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.5959e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.5974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5976e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 1.5970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5971e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5964e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5957e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 1.5956e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5955e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5953e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 1.5950e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5941e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 1.5913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5898e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5888e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5880e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.5869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5860e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5852e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.5846e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5826e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 1.5817e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5806e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5782e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 1.5767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5742e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 1.5729e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5717e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5707e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5694e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 1.5682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5671e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 1.5644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5614e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 1.5600e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5585e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5573e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5561e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5538e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5527e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5516e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5504e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5468e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5443e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5420e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5408e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5396e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5372e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5359e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5347e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5334e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5309e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5296e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5284e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5272e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5248e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5235e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5223e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5210e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5198e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5171e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5159e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5123e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5101e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5090e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5078e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.5067e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5056e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5044e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.5032e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5021e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.5010e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4999e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4989e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4979e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4969e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4948e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4928e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4907e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4897e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4887e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.3706e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4101 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 991/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.8045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.7166e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 1.6965e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.6060e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.7726e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.8652e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.9129e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 1.9323e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9403e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9432e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.9379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9298e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 1.9144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.9035e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.8915e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 1.8790e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8659e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8523e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8384e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 1.8249e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8146e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.8045e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 1.7958e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.7879e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.7810e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.7736e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 1.7663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.7597e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.7529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 1.7461e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7392e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 1.7261e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 1.7197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7135e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7076e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 1.7024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6973e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6921e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 1.6869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6818e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6767e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6714e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 1.6662e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6612e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6562e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 1.6514e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6469e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6426e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 1.6381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6337e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6252e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 1.6211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 1.6088e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 298ms/step - accuracy: 1.0000 - loss: 1.6049e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.6011e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 1.5938e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5901e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 1.5837e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5805e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5773e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 1.5741e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5709e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5648e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 1.5619e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5563e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 1.5535e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5507e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 1.5452e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5425e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5397e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5370e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 1.5343e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5289e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 1.5263e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5238e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5213e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 1.5188e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5164e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5140e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5116e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.5092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5069e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5046e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.5024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.5002e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4981e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.4960e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4939e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4917e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4896e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.4876e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4855e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4834e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.4814e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4795e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4775e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.4756e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4737e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4718e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4700e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.4681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4663e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4645e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.4626e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4608e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4590e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.4554e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.2455e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9772 - val_loss: 0.4118 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 992/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.3533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.4173e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.3913e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.3360e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2990e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 300ms/step - accuracy: 1.0000 - loss: 1.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.2092e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.1849e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1512e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1459e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.1424e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1379e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1315e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1286e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 1.1255e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1230e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1197e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1169e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 1.1260e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1333e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1492e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 1.1634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1760e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1869e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 1.1974e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2064e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2142e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 1.2211e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 1.2269e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2321e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 1.2369e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 1.2409e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2445e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 1.2477e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 1.2505e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2529e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2565e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 1.2579e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2591e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 1.2613e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2622e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 1.2635e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2642e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2651e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2658e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 1.2666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2673e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2678e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 300ms/step - accuracy: 1.0000 - loss: 1.2682e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2684e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 300ms/step - accuracy: 1.0000 - loss: 1.2688e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 300ms/step - accuracy: 1.0000 - loss: 1.2690e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2689e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2687e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 1.2685e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2681e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2677e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 1.2672e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2666e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2661e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2656e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 1.2649e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2644e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2639e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 1.2634e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2629e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2623e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 1.2617e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2610e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2603e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2596e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 1.2588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2580e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2572e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 1.2564e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2557e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2549e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 1.2541e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 1.2533e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2524e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2515e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 1.2506e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2497e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2489e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 1.2480e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2472e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2464e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 1.2456e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2448e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2439e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2431e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 1.2423e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2415e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2406e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 1.2398e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2390e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2381e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 1.2373e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2365e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2357e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2349e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 1.2340e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2332e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2324e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 1.2316e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2307e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2299e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2291e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 1.2282e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 1.1287e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4129 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 993/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 9.9270e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0823e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 35s 306ms/step - accuracy: 1.0000 - loss: 1.1185e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 35s 304ms/step - accuracy: 1.0000 - loss: 1.1094e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 303ms/step - accuracy: 1.0000 - loss: 1.0970e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.0827e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 34s 302ms/step - accuracy: 1.0000 - loss: 1.0698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0568e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 301ms/step - accuracy: 1.0000 - loss: 1.0416e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 33s 300ms/step - accuracy: 1.0000 - loss: 1.0294e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0212e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0128e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 1.0059e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0024e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 1.0004e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 300ms/step - accuracy: 1.0000 - loss: 9.9699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.9341e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8945e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8617e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 30s 300ms/step - accuracy: 1.0000 - loss: 9.8312e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.8010e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 300ms/step - accuracy: 1.0000 - loss: 9.7571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7171e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 300ms/step - accuracy: 1.0000 - loss: 9.7050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6965e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6867e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6757e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 27s 300ms/step - accuracy: 1.0000 - loss: 9.6631e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6500e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 300ms/step - accuracy: 1.0000 - loss: 9.6206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.6063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5937e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 300ms/step - accuracy: 1.0000 - loss: 9.5792e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.5661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.5547e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 300ms/step - accuracy: 1.0000 - loss: 9.5465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5212e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 300ms/step - accuracy: 1.0000 - loss: 9.5121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.5029e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 300ms/step - accuracy: 1.0000 - loss: 9.4828e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.4749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.4664e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 300ms/step - accuracy: 1.0000 - loss: 9.4599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.4538e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.4487e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 300ms/step - accuracy: 1.0000 - loss: 9.4420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 9.4354e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4285e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4214e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 9.4142e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.4068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.3996e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 9.3928e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.3862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.3829e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.3802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 9.3781e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.3753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.3720e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 9.3691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3668e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3637e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 9.3602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3570e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3512e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 9.3483e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.3458e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.3441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 9.3422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3401e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3398e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 9.3392e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3353e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 9.3342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 9.3300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3275e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 9.3259e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3207e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 9.3184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3161e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 9.3117e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3096e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 9.3063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 9.3046e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.3025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.3004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 9.2980e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.2954e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.2926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 9.2899e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.2874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.2847e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 9.2821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.2795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.2773e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.2747e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 9.2719e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.2692e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.2666e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 9.2638e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.2609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.2581e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.2554e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 9.2527e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.9307e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4151 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 994/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 1.0195e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.2267e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2510e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 298ms/step - accuracy: 1.0000 - loss: 1.2144e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.1861e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1588e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1345e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 1.1113e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 1.0892e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0698e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0546e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 1.0414e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0311e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0224e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 1.0152e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0099e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 1.0043e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 9.9953e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.9467e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 296ms/step - accuracy: 1.0000 - loss: 9.8939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 9.8390e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 8.6878e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6812e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 8.6675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6543e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 8.6414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 8.6236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6182e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6131e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 8.6085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.6036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 8.5894e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5798e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 8.5752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5711e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5669e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 8.5627e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5587e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5508e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 8.5469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5389e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 8.5348e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5226e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 8.5186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 8.0440e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4163 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 995/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 1.4000e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 300ms/step - accuracy: 1.0000 - loss: 1.2839e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.2036e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.1310e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 1.0801e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0412e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 1.0093e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 9.8340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.6016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 9.4073e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.2580e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 9.1201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 9.0087e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.9534e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 8.9067e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.8495e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.7946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.7384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 8.6857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.6336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.5805e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 8.5330e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4430e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.4017e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 8.3686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.3406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.3127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 8.2851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.2588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.2333e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.2085e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 8.1827e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.1589e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.1383e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 8.1181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.1013e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.0858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 8.0726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0586e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0295e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 8.0157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 8.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9879e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 7.9740e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.9611e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.9485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 7.9365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.9251e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.9143e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.9030e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 7.8919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.8809e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.8700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 7.8592e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.8484e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.8381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 7.8290e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.8197e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.8107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.8024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 7.7974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.7919e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.7868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 300ms/step - accuracy: 1.0000 - loss: 7.7821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.7779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.7738e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 300ms/step - accuracy: 1.0000 - loss: 7.7693e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.7649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.7610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.7571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 300ms/step - accuracy: 1.0000 - loss: 7.7535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.7503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.7477e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 300ms/step - accuracy: 1.0000 - loss: 7.7445e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.7415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.7385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 300ms/step - accuracy: 1.0000 - loss: 7.7356e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.7331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.7302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.7274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 300ms/step - accuracy: 1.0000 - loss: 7.7250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.7225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.7204e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 300ms/step - accuracy: 1.0000 - loss: 7.7184e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.7168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.7149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 300ms/step - accuracy: 1.0000 - loss: 7.7129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7066e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 300ms/step - accuracy: 1.0000 - loss: 7.7041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.7015e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.6990e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 300ms/step - accuracy: 1.0000 - loss: 7.6967e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.6946e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.6927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 300ms/step - accuracy: 1.0000 - loss: 7.6910e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.6890e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.6868e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.6844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 300ms/step - accuracy: 1.0000 - loss: 7.6822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.6803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.6783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 300ms/step - accuracy: 1.0000 - loss: 7.6763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.6749e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.6734e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 300ms/step - accuracy: 1.0000 - loss: 7.6722e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.6710e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.6699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.6686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 300ms/step - accuracy: 1.0000 - loss: 7.6674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.6661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.6649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 300ms/step - accuracy: 1.0000 - loss: 7.6635e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6621e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6607e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6597e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - accuracy: 1.0000 - loss: 7.6588e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 304ms/step - accuracy: 1.0000 - loss: 7.5482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4179 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 996/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 302ms/step - accuracy: 1.0000 - loss: 9.0958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 1.0112e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 1.0154e-07 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.8127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 9.4818e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 9.1888e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 8.9311e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.7148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 8.5261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.3796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.2688e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 8.1609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 8.0855e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 8.0236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 296ms/step - accuracy: 1.0000 - loss: 7.9737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 296ms/step - accuracy: 1.0000 - loss: 7.9195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.8674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 7.8149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.7647e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.7169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.6682e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 7.6237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.5874e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.5546e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 297ms/step - accuracy: 1.0000 - loss: 7.5287e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.5086e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4927e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4746e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 7.4564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4385e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 297ms/step - accuracy: 1.0000 - loss: 7.4034e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3684e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 297ms/step - accuracy: 1.0000 - loss: 7.3530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.3381e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.3241e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.3115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 297ms/step - accuracy: 1.0000 - loss: 7.3004e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.2891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.2777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 297ms/step - accuracy: 1.0000 - loss: 7.2670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.2563e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.2464e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 297ms/step - accuracy: 1.0000 - loss: 7.2352e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.2243e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.2141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.2035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 297ms/step - accuracy: 1.0000 - loss: 7.1963e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.1906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.1863e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 297ms/step - accuracy: 1.0000 - loss: 7.1811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.1758e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.1700e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.1639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 297ms/step - accuracy: 1.0000 - loss: 7.1582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.1520e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.1461e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 297ms/step - accuracy: 1.0000 - loss: 7.1415e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.1365e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.1323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 7.1281e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 7.1252e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.1219e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.1185e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 298ms/step - accuracy: 1.0000 - loss: 7.1148e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.1111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.1071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 298ms/step - accuracy: 1.0000 - loss: 7.1035e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0964e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 298ms/step - accuracy: 1.0000 - loss: 7.0926e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0891e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0862e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0834e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 1.0000 - loss: 7.0804e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0743e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 298ms/step - accuracy: 1.0000 - loss: 7.0716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0686e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0654e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0625e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 298ms/step - accuracy: 1.0000 - loss: 7.0598e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.0571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.0545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 1.0000 - loss: 7.0523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.0505e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.0485e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 298ms/step - accuracy: 1.0000 - loss: 7.0463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0441e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0418e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0394e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 298ms/step - accuracy: 1.0000 - loss: 7.0368e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.0342e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.0319e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 298ms/step - accuracy: 1.0000 - loss: 7.0296e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.0278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.0261e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 298ms/step - accuracy: 1.0000 - loss: 7.0257e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0250e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 298ms/step - accuracy: 1.0000 - loss: 7.0224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.0213e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.0200e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 298ms/step - accuracy: 1.0000 - loss: 7.0190e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.0181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.0172e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 298ms/step - accuracy: 1.0000 - loss: 7.0162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0156e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0149e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0141e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 7.0132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.0121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 7.0110e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 7.0097e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.0083e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.0068e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.0055e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 7.0040e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 6.8300e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4195 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 997/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 9.0078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 8.5762e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 8.7463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 294ms/step - accuracy: 1.0000 - loss: 8.5188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 8.2995e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 294ms/step - accuracy: 1.0000 - loss: 8.1294e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.9633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 7.8078e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.6610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 296ms/step - accuracy: 1.0000 - loss: 7.5336e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.4366e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 7.3465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.2704e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 7.2077e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 298ms/step - accuracy: 1.0000 - loss: 7.1564e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.1063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0695e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 298ms/step - accuracy: 1.0000 - loss: 7.0011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.9766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.9535e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 6.9318e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.9146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.9037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.8942e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 6.8883e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.8837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.8753e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 6.8667e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8482e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.8393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8209e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 6.8124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.8041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7979e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.7896e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7803e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.7750e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7689e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.7591e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7565e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7528e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.7497e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7468e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7451e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7429e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.7406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7382e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7357e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.7328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.7298e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.7267e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.7237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7205e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7175e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7145e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.7121e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.7093e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.7063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 6.7032e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.6999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.6966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 6.6929e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6892e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6858e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 6.6785e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 6.6712e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6702e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6691e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 6.6678e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6674e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6655e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 6.6649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6641e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 6.6626e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.6619e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.6612e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 6.6609e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6603e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6584e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 6.6571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.6560e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.6548e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 6.6536e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.6524e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.6513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 6.6503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6490e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6476e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 6.6442e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.6422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.6400e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 6.6379e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.6359e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.6340e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 6.6321e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6304e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6289e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6274e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 6.6263e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6253e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6242e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 6.6232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6221e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6210e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 6.6187e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 6.4794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4212 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 998/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 301ms/step - accuracy: 1.0000 - loss: 7.0058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 35s 299ms/step - accuracy: 1.0000 - loss: 7.3741e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.5514e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.4111e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.3384e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 34s 299ms/step - accuracy: 1.0000 - loss: 7.2615e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.1605e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 7.0599e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 299ms/step - accuracy: 1.0000 - loss: 6.9558e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.8640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 300ms/step - accuracy: 1.0000 - loss: 6.7877e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.7123e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 32s 299ms/step - accuracy: 1.0000 - loss: 6.6479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.6058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 31s 299ms/step - accuracy: 1.0000 - loss: 6.5542e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.5277e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4999e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 299ms/step - accuracy: 1.0000 - loss: 6.4727e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4463e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.4181e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3921e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 29s 299ms/step - accuracy: 1.0000 - loss: 6.3723e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3511e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3328e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 28s 299ms/step - accuracy: 1.0000 - loss: 6.3169e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.3064e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2934e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 299ms/step - accuracy: 1.0000 - loss: 6.2796e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2649e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2510e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 26s 299ms/step - accuracy: 1.0000 - loss: 6.2218e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.2071e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1944e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 299ms/step - accuracy: 1.0000 - loss: 6.1822e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.1708e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.1602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 299ms/step - accuracy: 1.0000 - loss: 6.1517e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1422e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1331e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1237e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 23s 299ms/step - accuracy: 1.0000 - loss: 6.1140e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.1041e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0938e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 299ms/step - accuracy: 1.0000 - loss: 6.0844e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0775e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 299ms/step - accuracy: 1.0000 - loss: 6.0633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0582e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0539e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0492e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 20s 299ms/step - accuracy: 1.0000 - loss: 6.0443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0395e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0345e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 299ms/step - accuracy: 1.0000 - loss: 6.0297e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0195e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 299ms/step - accuracy: 1.0000 - loss: 6.0150e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0103e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0060e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 6.0021e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 17s 299ms/step - accuracy: 1.0000 - loss: 5.9987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9950e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9912e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 299ms/step - accuracy: 1.0000 - loss: 5.9875e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9835e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9795e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 299ms/step - accuracy: 1.0000 - loss: 5.9752e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9709e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9670e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9634e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 14s 299ms/step - accuracy: 1.0000 - loss: 5.9602e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9549e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 299ms/step - accuracy: 1.0000 - loss: 5.9523e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9496e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 299ms/step - accuracy: 1.0000 - loss: 5.9443e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9419e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9393e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9367e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 11s 299ms/step - accuracy: 1.0000 - loss: 5.9344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9323e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9302e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 299ms/step - accuracy: 1.0000 - loss: 5.9283e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9265e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9245e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 299ms/step - accuracy: 1.0000 - loss: 5.9225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9206e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9186e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9168e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 299ms/step - accuracy: 1.0000 - loss: 5.9147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9127e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9109e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 299ms/step - accuracy: 1.0000 - loss: 5.9091e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9058e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 299ms/step - accuracy: 1.0000 - loss: 5.9048e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9036e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9024e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.9011e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 299ms/step - accuracy: 1.0000 - loss: 5.8997e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8982e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8966e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 299ms/step - accuracy: 1.0000 - loss: 5.8952e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8939e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 299ms/step - accuracy: 1.0000 - loss: 5.8911e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8898e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8885e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8871e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 299ms/step - accuracy: 1.0000 - loss: 5.8857e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8842e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8826e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 299ms/step - accuracy: 1.0000 - loss: 5.8811e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8794e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8777e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8761e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 1.0000 - loss: 5.8744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 303ms/step - accuracy: 1.0000 - loss: 5.6725e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4220 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 999/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 298ms/step - accuracy: 1.0000 - loss: 5.7098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 5.9475e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.1157e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 296ms/step - accuracy: 1.0000 - loss: 6.0594e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 33s 296ms/step - accuracy: 1.0000 - loss: 5.9460e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.8503e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.7574e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 295ms/step - accuracy: 1.0000 - loss: 5.6633e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.5745e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.5154e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 295ms/step - accuracy: 1.0000 - loss: 5.4882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 31s 294ms/step - accuracy: 1.0000 - loss: 5.4622e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.4413e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 295ms/step - accuracy: 1.0000 - loss: 5.4273e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.4262e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.4229e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.4196e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 295ms/step - accuracy: 1.0000 - loss: 5.4215e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.4224e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.4310e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 295ms/step - accuracy: 1.0000 - loss: 5.4369e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.4420e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.4479e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.4513e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 295ms/step - accuracy: 1.0000 - loss: 5.4544e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.4585e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.4675e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 295ms/step - accuracy: 1.0000 - loss: 5.4732e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.4782e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.4817e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 295ms/step - accuracy: 1.0000 - loss: 5.4839e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 5.4851e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - accuracy: 1.0000 - loss: 5.4845e-08 - 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mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 296ms/step - accuracy: 1.0000 - loss: 5.4807e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4791e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4770e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4751e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 296ms/step - accuracy: 1.0000 - loss: 5.4737e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.4721e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 296ms/step - accuracy: 1.0000 - loss: 5.4708e-08 - 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0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4301e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.4292e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4280e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4258e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.4248e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4236e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.4211e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4202e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4192e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.4183e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4174e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4166e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4160e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.4153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.4146e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.4139e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 297ms/step - accuracy: 1.0000 - loss: 5.4132e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4124e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 1.0000 - loss: 5.4101e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 301ms/step - accuracy: 1.0000 - loss: 5.3324e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4236 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 Epoch 1000/1000  1/120 ━━━━━━━━━━━━━━━━━━━━ 35s 297ms/step - accuracy: 1.0000 - loss: 5.2779e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  2/120 ━━━━━━━━━━━━━━━━━━━━ 34s 295ms/step - accuracy: 1.0000 - loss: 5.3660e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  3/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.4491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  4/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.3432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  5/120 ━━━━━━━━━━━━━━━━━━━━ 34s 297ms/step - accuracy: 1.0000 - loss: 5.2469e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  6/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.1715e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  7/120 ━━━━━━━━━━━━━━━━━━━━ 33s 297ms/step - accuracy: 1.0000 - loss: 5.1057e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  8/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 5.0432e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  9/120 ━━━━━━━━━━━━━━━━━━━━ 33s 298ms/step - accuracy: 1.0000 - loss: 4.9846e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  10/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9530e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  11/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9377e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  12/120 ━━━━━━━━━━━━━━━━━━━━ 32s 297ms/step - accuracy: 1.0000 - loss: 4.9223e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  13/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9138e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  14/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9165e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  15/120 ━━━━━━━━━━━━━━━━━━━━ 31s 297ms/step - accuracy: 1.0000 - loss: 4.9232e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  16/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  17/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9198e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  18/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9147e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  19/120 ━━━━━━━━━━━━━━━━━━━━ 30s 297ms/step - accuracy: 1.0000 - loss: 4.9129e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  20/120 ━━━━━━━━━━━━━━━━━━━━ 29s 298ms/step - accuracy: 1.0000 - loss: 4.9088e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  21/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.9025e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  22/120 ━━━━━━━━━━━━━━━━━━━━ 29s 297ms/step - accuracy: 1.0000 - loss: 4.8974e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  23/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8958e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  24/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  25/120 ━━━━━━━━━━━━━━━━━━━━ 28s 298ms/step - accuracy: 1.0000 - loss: 4.8941e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  26/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.8968e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  27/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.9033e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  28/120 ━━━━━━━━━━━━━━━━━━━━ 27s 297ms/step - accuracy: 1.0000 - loss: 4.9076e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  29/120 ━━━━━━━━━━━━━━━━━━━━ 27s 298ms/step - accuracy: 1.0000 - loss: 4.9107e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  30/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.9162e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  31/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.9199e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  32/120 ━━━━━━━━━━━━━━━━━━━━ 26s 298ms/step - accuracy: 1.0000 - loss: 4.9233e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  33/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9269e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  34/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9313e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  35/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9363e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  36/120 ━━━━━━━━━━━━━━━━━━━━ 25s 298ms/step - accuracy: 1.0000 - loss: 4.9406e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  37/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9457e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  38/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9516e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  39/120 ━━━━━━━━━━━━━━━━━━━━ 24s 298ms/step - accuracy: 1.0000 - loss: 4.9596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  40/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  41/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9716e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  42/120 ━━━━━━━━━━━━━━━━━━━━ 23s 298ms/step - accuracy: 1.0000 - loss: 4.9766e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  43/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9815e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  44/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9853e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  45/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9880e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  46/120 ━━━━━━━━━━━━━━━━━━━━ 22s 298ms/step - accuracy: 1.0000 - loss: 4.9906e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  47/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9936e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  48/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9961e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  49/120 ━━━━━━━━━━━━━━━━━━━━ 21s 298ms/step - accuracy: 1.0000 - loss: 4.9987e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  50/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.0016e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  51/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.0050e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  52/120 ━━━━━━━━━━━━━━━━━━━━ 20s 298ms/step - accuracy: 1.0000 - loss: 5.0074e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  53/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.0116e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  54/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.0153e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  55/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.0188e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  56/120 ━━━━━━━━━━━━━━━━━━━━ 19s 298ms/step - accuracy: 1.0000 - loss: 5.0225e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  57/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.0266e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  58/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.0306e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  59/120 ━━━━━━━━━━━━━━━━━━━━ 18s 298ms/step - accuracy: 1.0000 - loss: 5.0344e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  60/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.0380e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  61/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.0414e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  62/120 ━━━━━━━━━━━━━━━━━━━━ 17s 297ms/step - accuracy: 1.0000 - loss: 5.0452e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  63/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0499e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  64/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0540e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  65/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0577e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  66/120 ━━━━━━━━━━━━━━━━━━━━ 16s 297ms/step - accuracy: 1.0000 - loss: 5.0610e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  67/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.0639e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  68/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.0665e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  69/120 ━━━━━━━━━━━━━━━━━━━━ 15s 297ms/step - accuracy: 1.0000 - loss: 5.0687e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  70/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.0707e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  71/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.0726e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  72/120 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - accuracy: 1.0000 - loss: 5.0744e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  73/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0763e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  74/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0783e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  75/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0802e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  76/120 ━━━━━━━━━━━━━━━━━━━━ 13s 297ms/step - accuracy: 1.0000 - loss: 5.0821e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  77/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0837e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  78/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0852e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  79/120 ━━━━━━━━━━━━━━━━━━━━ 12s 297ms/step - accuracy: 1.0000 - loss: 5.0866e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  80/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0882e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  81/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0895e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  82/120 ━━━━━━━━━━━━━━━━━━━━ 11s 297ms/step - accuracy: 1.0000 - loss: 5.0909e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  83/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0925e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  84/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0940e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  85/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0955e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  86/120 ━━━━━━━━━━━━━━━━━━━━ 10s 297ms/step - accuracy: 1.0000 - loss: 5.0972e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  87/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.0991e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000   88/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.1007e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  89/120 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 1.0000 - loss: 5.1022e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  90/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.1037e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  91/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.1051e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  92/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.1063e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  93/120 ━━━━━━━━━━━━━━━━━━━━ 8s 297ms/step - accuracy: 1.0000 - loss: 5.1072e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  94/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.1081e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  95/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.1089e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  96/120 ━━━━━━━━━━━━━━━━━━━━ 7s 297ms/step - accuracy: 1.0000 - loss: 5.1098e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  97/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.1106e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  98/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.1115e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000  99/120 ━━━━━━━━━━━━━━━━━━━━ 6s 297ms/step - accuracy: 1.0000 - loss: 5.1159e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 100/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.1201e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 101/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.1240e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 102/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.1278e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 103/120 ━━━━━━━━━━━━━━━━━━━━ 5s 297ms/step - accuracy: 1.0000 - loss: 5.1314e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 104/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.1347e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 105/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.1378e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 106/120 ━━━━━━━━━━━━━━━━━━━━ 4s 297ms/step - accuracy: 1.0000 - loss: 5.1408e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 107/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.1437e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 108/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.1465e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 109/120 ━━━━━━━━━━━━━━━━━━━━ 3s 297ms/step - accuracy: 1.0000 - loss: 5.1491e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 110/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.1518e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 111/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.1545e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 112/120 ━━━━━━━━━━━━━━━━━━━━ 2s 297ms/step - accuracy: 1.0000 - loss: 5.1571e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 113/120 ━━━━━━━━━━━━━━━━━━━━ 2s 298ms/step - accuracy: 1.0000 - loss: 5.1596e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 114/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.1618e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 115/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.1640e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 116/120 ━━━━━━━━━━━━━━━━━━━━ 1s 298ms/step - accuracy: 1.0000 - loss: 5.1661e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 117/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.1680e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 118/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.1699e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 119/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.1718e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - accuracy: 1.0000 - loss: 5.1735e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 120/120 ━━━━━━━━━━━━━━━━━━━━ 36s 302ms/step - accuracy: 1.0000 - loss: 5.3786e-08 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000 - val_accuracy: 0.9773 - val_loss: 0.4245 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4992 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 473ms/step 1/1 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